Communication Rounds Research Articles

Local certification of unitary operations

In this work, we analyze the local certification of unitary quantum channels, which is a natural extension of quantum hypothesis testing. A particular case of a quantum channel operating on two systems corresponding to product states at the input, is considered. The goal is to minimize the probability of the type II error, given a specified maximum probability of the type I error, considering assistance through entanglement with auxiliary systems. Our result indicates connection of the local certification problem with a product numerical range of unitary matrices. We show that the optimal local strategy does not need usage of auxiliary systems and requires only single round of one-way classical communication. Moreover, we compare local and global certification strategies and show that typically local strategies are optimal, yet in some extremal cases, where global strategies make no errors, local ones may fail miserably. Finally, some application for local certification of von Neumann measurements are discussed as well.

RNEP: Random Node Entropy Pairing for Efficient Decentralized Training with Non-IID Local Data

The proliferation of edge devices and advancements in Internet of Things technology have created a vast array of distributed data sources, necessitating machine learning models that can operate closer to the point of data generation. Traditional centralized machine learning approaches often struggle with real-time big data applications, such as climate prediction and traffic simulation, owing to high communication costs. In this study, we introduce random node entropy pairing, a novel distributed learning method for artificial neural networks tailored to distributed computing environments. This method reduces communication overhead and addresses data imbalance by enabling nodes to exchange only the weights of their local models with randomly selected peers during each communication round, rather than sharing the entire dataset. Our findings indicate that this approach significantly reduces communication costs while maintaining accuracy, even when learning from non-IID local data. Furthermore, we explored additional learning strategies that enhance system accuracy by leveraging the characteristics of this method. The results demonstrate that random node entropy pairing is an effective and efficient solution for distributed learning in environments where communication costs and data distribution present significant challenges.

Advanced Optimization Techniques for Federated Learning on Non-IID Data

Federated learning enables model training on multiple clients locally, without the need to transfer their data to a central server, thus ensuring data privacy. In this paper, we investigate the impact of Non-Independent and Identically Distributed (non-IID) data on the performance of federated training, where we find a reduction in accuracy of up to 29% for neural networks trained in environments with skewed non-IID data. Two optimization strategies are presented to address this issue. The first strategy focuses on applying a cyclical learning rate to determine the learning rate during federated training, while the second strategy develops a sharing and pre-training method on augmented data in order to improve the efficiency of the algorithm in the case of non-IID data. By combining these two methods, experiments show that the accuracy on the CIFAR-10 dataset increased by about 36% while achieving faster convergence by reducing the number of required communication rounds by 5.33 times. The proposed techniques lead to improved accuracy and faster model convergence, thus representing a significant advance in the field of federated learning and facilitating its application to real-world scenarios.

Constant-Round YOSO MPC Without Setup

YOSO MPC (Gentry et al., Crypto 2021) is a new MPC framework where each participant can speak at most once. This models an adaptive adversary’s ability to watch the network and corrupt or destroy parties it deems significant based on their communication. By using private channels to anonymous receivers (e.g. by encrypting to a public key whose owner is unknown), the communication complexity of YOSO MPC can scale sublinearly with the total number N of available parties, even when the adversary’s corruption threshold is linear in N (e.g. just under N/2). It was previously an open problem whether YOSO MPC can achieve guaranteed output delivery in a constant number of rounds without relying on trusted setup. In this work, we show that this can indeed be accomplished. We demonstrate three different approaches: the first two (which we call YaOSO and YOSO-GLS) use two and three rounds of communication, respectively. Our third approach (which we call YOSO-LHSS) uses O(d) rounds, where d is the multiplicative depth of the circuit being evaluated; however, it can be used to bootstrap any constant-round YOSO protocol that requires setup, by generating that setup within YOSO-LHSS. Though YOSO-LHSS requires more rounds than our first two approaches, it may be more practical, since the zero knowledge proofs it employs are more efficient to instantiate. As a contribution of independent interest, we introduce a verifiable state propagation UC functionality, which allows parties to send private message which are verifiably derived in the “correct” way (according to the protocol in question) to anonymous receivers. This is a natural functionality to build YOSO protocols on top of.

Practical and Robust Federated Learning With Highly Scalable Regression Training.

Privacy-preserving federated learning, as one of the privacy-preserving computation techniques, is a promising distributed and privacy-preserving machine learning (ML) approach for Internet of Medical Things (IoMT), due to its ability to train a regression model without collecting raw data of data owners (DOs). However, traditional interactive federated regression training (IFRT) schemes rely on multiple rounds of communication to train a global model and are still under various privacy and security threats. To overcome these problems, several noninteractive federated regression training (NFRT) schemes have been proposed and applied in a variety of scenarios. However, there are still several challenges: 1) how to protect the privacy of DOs' local dataset; 2) how to realize highly scalable regression training without linear dependence on sample dimension; 3) how to tolerate DOs' dropout; and 4) how to enable DOs to verify the correctness of aggregated results returned from the cloud service provider (CSP). In this article, we propose two practical noninteractive federated learning schemes with privacy-preserving for IoMT, named homomorphic encryption based NFRT (HE-NFRT) and double-masking protocol based NFRT (Mask-NFRT), respectively, which are based on a comprehensive consideration of NFRT, privacy concerns, high-efficiency, robustness, and verification mechanism. The security analyses display that our proposed schemes are able to protect the privacy of DOs' local training data, resist collusion attack, and support strong verification to each DO. The performance evaluation results demonstrate that our proposed HE-NFRT scheme is desirable for a high-dimensional and high-security IoMT application while Mask-NFRT scheme is desirable for a high-dimensional and large-scale IoMT application.

Federated Learning of Generalized Linear Causal Networks.

Causal discovery, the inference of causal relations among variables from data, is a fundamental problem of science. Nowadays, due to an increased awareness of data privacy concerns, there has been a shift towards distributed data collection, processing and storage. To meet the pressing need for distributed causal discovery, we propose a novel federated DAG learning method called distributed annealing on regularized likelihood score (DARLS) to learn a causal graph from data stored on multiple clients. DARLS simulates an annealing process to search over the space of topological sorts, where the optimal graphical structure compatible with a sort is found by distributed optimization. This distributed optimization relies on multiple rounds of communication between local clients and a central server to estimate the graphical structure. We establish its convergence to the solution obtained by an oracle with access to all the data. To the best of our knowledge, DARLS is the first distributed method for learning causal graphs with such finite-sample oracle guarantees. To establish the consistency of DARLS, we also derive new identifiability results for causal graphs parameterized by generalized linear models, which could be of independent interest. Through extensive simulation studies and a real-world application, we show that DARLS outperforms existing federated learning methods and is comparable to oracle methods on pooled data, demonstrating its great advantages in estimating causal networks from distributed data.

Adaptive compressed learning boosts both efficiency and utility of differentially private federated learning

In the federated learning (FL) research field, current research is confronted with several pivotal challenges, e.g., data privacy, model utility and communication efficiency. Furthermore, these challenges are further amplified by statistical data heterogeneous in the FL system. Thus, a novel Communication-efficient and Utility-assured Gaussian differential privacy-based Personalized Federated Adaptive Compressed Learning method, called CUG-PFACL, is proposed. Specifically, an end-to-end local adaptive compressed learning strategy is designed, including three crucial modules, namely the measurement matrix, the personalized compressed data transformation and the local model. Especially, jointly training the measurement matrix module and the personalized compressed data transformation module can mitigate the inherent statistical heterogeneity while preserving all important characteristics of the compressed private data of each local client, and alleviate the additional heterogeneity induced by Gaussian differential privacy in each global communication round. Numerous experimental simulation and comparisons demonstrate that CUG-PFACL has three notable advantages: data privacy guarantee, enhanced personalized model utility and high-efficient communication.

FedDSS: A data-similarity approach for client selection in horizontal federated learning

Background and objectiveFederated learning (FL) is an emerging distributed learning framework allowing multiple clients (hospitals, institutions, smart devices, etc.) to collaboratively train a centralized machine learning model without disclosing personal data. It has the potential to address several healthcare challenges, including a lack of training data, data privacy, and security concerns. However, model learning under FL is affected by non-i.i.d. data, leading to severe model divergence and reduced performance due to the varying client's data distributions. To address this problem, we propose FedDSS, Federated Data Similarity Selection, a framework that uses a data-similarity approach to select clients, without compromising client data privacy. MethodsFedDSS comprises a statistical-based data similarity metric, a N-similar-neighbor network, and a network-based selection strategy. We assessed FedDSS' performance against FedAvg's in i.i.d. and non-i.i.d. settings with two public pediatric sepsis datasets (PICD and MIMICIII). Selection fairness was measured using entropy. Simulations were repeated five times to evaluate average loss, true positive rate (TPR), and entropy. ResultsIn i.i.d setting on PICD, FedDSS achieved a higher TPR starting from the 9th round and surpassing 0.6 three rounds earlier than FedAvg. On MIMICIII, FedDSS's loss decreases significantly from the 13th round, with TPR > 0.8 by the 2nd round, two rounds ahead of FedAvg (at the 4th round). In the non-i.i.d. setting, FedDSS achieved TPR > 0.7 by the 4th and > 0.8 by the 7th round, earlier than FedAvg (at the 5th and 11th rounds). In both settings, FedDSS showed reasonable fairness (entropy of 2.2 and 2.1). ConclusionWe demonstrated that FedDSS contributes to improved learning in FL by achieving faster convergence, reaching the desired TPR with fewer communication rounds, and potentially enhancing sepsis prediction (TPR) over FedAvg.

Stabilizing and Accelerating Federated Learning on Heterogeneous Data With Partial Client Participation.

Federated learning (FL) commonly encourages the clients to perform multiple local updates before the global aggregation, thus avoiding frequent model exchanges and relieving the communication bottleneck between the server and clients. Though empirically effective, the negative impact of multiple local updates on the stability of FL is not thoroughly studied, which may result in a globally unstable and slow convergence. Based on sensitivity analysis, we define in this paper a local-update stability index for the general FL, as measured by the maximum inter-client model discrepancy after the multiple local updates that mainly stems from the data heterogeneity. It enables to determine how much the variation of client's models with multiple local updates may influence the global model, and can also be linked with the convergence and generalization. We theoretically derive the proposed local-update stability for current state-of-the-art FL methods, providing possible insight to understanding their motivation and limitation from a new perspective of stability. For example, naively executing the parallel acceleration locally at clients would harm the local-update stability. Motivated by this, we then propose a novel accelerated yet stabilized FL algorithm (named FedANAG) based on the server- and client-level Nesterov accelerated gradient (NAG). In FedANAG, the global and local momenta are elaborately designed and alternatively updated, while the stability of local update is enhanced with help of the global momentum. We prove the convergence of FedANAG for strongly convex, general convex and non-convex settings. We then conduct evaluations on both the synthetic and real-world datasets to first validate our proposed local-update stability. The results further show that across various data heterogeneity and client participation ratios, FedANAG not only accelerates the global convergence by reducing the required number of communication rounds to a target accuracy, but converges to an eventually higher accuracy.

Dominant point-based sequential and parallel algorithms for the multiple sequential substring constrained-LCS problem

The Longest Common Subsequence (LCS) problem is a well-known and studied problem in computer science and bioinformatics. It consists in finding the longest subsequence that is common to two or more given sequences. In this paper, we address the problem of finding all LCS for the Sequential Substring Constrained-LCS (SSCLCS) problem, called the Multiple SSCLCS problem. To solve this problem, we first propose a dominant point-based sequential algorithm, designed on a new Leveled Direct Acyclic Graph (DAG) that gives the correct evaluation order of subproblems to avoid redundancy due to overlap. Depending on whether the constraints may overlap or not, it requires O ( S | Σ | K + 1 + r + n | Σ |) and O ( S | Σ | K + 1 + n | Σ |) time with \(O(Max\_level+n|\Sigma |) \) space. S is the number of partial SSCLCS in a node, K is the number of DAG levels, n is the length of sequences, r is the total length of constraints, \(Max\_level \) is the number of nodes in the largest level of the DAG, and | Σ | is the length of the alphabet. Then, we derive a coarse-grained multicomputer parallel solution requiring \(O\left(\frac{S|\Sigma |^{K+1}+r+n|\Sigma |}{p}\right) \) and \(O\left(\frac{S|\Sigma |^{K+1}+n|\Sigma |}{p}\right) \) execution time, \(O(Max\_level+n|\Sigma |) \) memory space and O ( K ) communication rounds. p is the number of processors. Experimental results showed that the parallel algorithm is respectively 14.43 × and 19.19 × faster than the sequential algorithm on 32 and 64 processors.

SoFL: Clustered Federated Learning Based on Dual Clustering for Heterogeneous Data

Federated Learning (FL) is an emerging privacy-preserving technology that enables training a global model beneficial to all participants without sharing their data. However, differences in data distributions among participants may undermine the stability and accuracy of the global model. To address this challenge, recent research proposes client clustering based on data distribution similarity, generating independent models for each cluster in order to enhance FL performance. Nevertheless, due to the uncertainty of participant identities, FL struggles to rapidly and accurately determine the clusters. Most of the existing algorithms distinguish clients by iterative clustering, which not only increases the computing cost of the server but also affects the convergence speed of the federation model. To address these shortcomings, in this paper, we propose a novel clustering-based FL method, SoFL. SoFL introduces SOM networks, improves the quality of cluster data, and eliminates redundant categories through secondary clustering, encouraging more similar clients to train together. Through this mechanism, SoFL completes the clustering task in one round of communication and speeds up the convergence of federated model training. Simulation results demonstrate that SoFL accurately and swiftly adapts to determine the clusters. In different non-IID settings, SoFL’s model accuracy improvements ranged from 9 to 18% compared to FedAvg and FedProx.

FedNIDS: A Federated Learning Framework for Packet-based Network Intrusion Detection System

Network intrusion detection systems (NIDS) play a critical role in discerning between benign and malicious network traffic. Deep neural networks (DNNs), anchored on large and diverse datasets, exhibit promise in enhancing the detection accuracy of NIDS by capturing intricate network traffic patterns. However, safeguarding distributed computer networks against emerging cyber threats is increasingly challenging. Despite the abundance of diverse network data, decentralization persists due to data privacy and security concerns. This confers an asymmetric advantage to adversaries, as distributed networks face the formidable task of securely and efficiently sharing non-independently and identically distributed data to counter cyber-attacks. To address this, we propose the Federated NIDS ( FedNIDS ), a novel two-stage framework that combines the power of federated learning and DNNs. It aims to enhance the detection accuracy of known attacks, robustness and resilience to novel attack patterns, and privacy preservation, using packet-level granular data. In the first stage, a global DNN model is collaboratively trained on distributed data, and the second stage adapts it to novel attack patterns. Our experiments on real-world intrusion datasets demonstrate the effectiveness of FedNIDS by achieving an average F1 score of 0.97 across distributed networks and quickly disseminating novel attack information within four rounds of communication.

Secure Dynamic Scheduling for Federated Learning in Underwater Wireless IoT Networks

Federated learning (FL) is a distributed machine learning approach that can enable Internet of Things (IoT) edge devices to collaboratively learn a machine learning model without explicitly sharing local data in order to achieve data clustering, prediction, and classification in networks. In previous works, some online multi-armed bandit (MAB)-based FL frameworks were proposed to enable dynamic client scheduling for improving the efficiency of FL in underwater wireless IoT networks. However, the security of online dynamic scheduling, which is especially essential for underwater wireless IoT, is increasingly being questioned. In this work, we study secure dynamic scheduling for FL frameworks that can protect against malicious clients in underwater FL-assisted wireless IoT networks. Specifically, in order to jointly optimize the communication efficiency and security of FL, we employ MAB-based methods and propose upper-confidence-bound-based smart contracts (UCB-SCs) and upper-confidence-bound-based smart contracts with a security prediction model (UCB-SCPs) to address the optimal scheduling scheme over time-varying underwater channels. Then, we give the upper bounds of the expected performance regret of the UCB-SC policy and the UCB-SCP policy; these upper bounds imply that the regret of the two proposed policies grows logarithmically over communication rounds under certain conditions. Our experiment shows that the proposed UCB-SC and UCB-SCP approaches significantly improve the efficiency and security of FL frameworks in underwater wireless IoT networks.

Blockchain-based conditional privacy-preserving authentication scheme using PUF for vehicular ad hoc networks

Vehicular ad hoc networks (VANET) have been the key indispensable module of the future intelligent transportation system. Security and privacy are two essential attributes that protect the safe driving of vehicles. Over the last two decades, numerous conditional privacy-preserving authentication schemes have been presented for the VANET environment. However, existing schemes have various limitations, including security issues, high storage overhead, and frequent interactions. In order to bridge these difficulties, this work combines physically unclonable function and blockchain technology to construct a conditional privacy-preserving authentication scheme for the VANET environment. Specifically, we combine physical unclonable function and dynamic pseudonym techniques to generate unique pseudonym IDs dynamically and private keys using physical unclonable function to enhance privacy protection and resist physical attack. To reduce the number of communication rounds during the verification process, we deployed lightweight blockchain nodes to avoid direct communication between the receiver and the blockchain network. The proposed scheme demonstrates resilience against various potential attacks through comprehensive security analysis and proof. Furthermore, performance metrics indicate that our scheme outperforms similar schemes, making it suitable for resource-constrained VANET.

An MLWE-Based Cut-and-Choose Oblivious Transfer Protocol.

The existing lattice-based cut-and-choose oblivious transfer protocol is constructed based on the learning-with-errors (LWE) problem, which generally has the problem of inefficiency. An efficient cut-and-choose oblivious transfer protocol is proposed based on the difficult module-learning-with-errors (MLWE) problem. Compression and decompression techniques are introduced in the LWE-based dual-mode encryption system to improve it to an MLWE-based dual-mode encryption framework, which is applied to the protocol as an intermediate scheme. Subsequently, the security and efficiency of the protocol are analysed, and the security of the protocol can be reduced to the shortest independent vector problem (SIVP) on the lattice, which is resistant to quantum attacks. Since the whole protocol relies on the polynomial ring of elements to perform operations, the efficiency of polynomial modulo multiplication can be improved by using fast Fourier transform (FFT). Finally, this paper compares the protocol with an LWE-based protocol in terms of computational and communication complexities. The analysis results show that the protocol reduces the computation and communication overheads by at least a factor of n while maintaining the optimal number of communication rounds under malicious adversary attacks.

Enhancing Transmission Efficiency in Non-IID Federated Learning by Integrating Quantization into an Existing Trustworthiness Model

Federated Learning (FL) is a distributed machine learning paradigm that enables multiple clients to collaboratively train a model without sharing their data, thus preserving privacy. In this approach, data is not transmitted between the client and the base station. Instead, a model is sent to each device, where it is trained locally, and then retransmitted back to the base station. Each iteration of this process is known as a communication round. However, FL faces challenges, especially when data is not independently and identically distributed (Non-IID). Non-IID data means that the data across clients can vary significantly in distribution, leading to situations where certain classes or features are overrepresented in some clients and underrepresented in others. This lack of uniformity quickly leads to biased model updates and reduced performance. To address this, previous studies have introduced trustworthiness metrics to ensure more reliable model aggregation, minimizing accuracy losses associated with Non-IID data. Another significant challenge is the high transmission load, as model updates between clients and the base station are resource-intensive. Model parameters are transmitted between clients and base stations, which can strain communication channels and slow down the entire process, especially when dealing with larger models and datasets. This communication overhead is a bottleneck that limits the scalability of FL, particularly in resource-constrained environments. Our research addresses these challenges by integrating quantization into the trustworthiness model specifically in a Non-IID scenario. Quantization reduces the precision of model parameters to minimize data transmission, resulting in more efficient communication. We applied different levels of quantization intensity to a model trained on the CIFAR-10 dataset and found that certain methods can significantly reduce transmission overhead without substantial accuracy loss. Our findings suggest that combining quantization with trustworthiness metrics can significantly enhance the efficiency and potentially improve the adoption of Federated Learning in resource-constrained environments.

Implementing and evaluating simple resampling techniques in federated learning for imbalanced data

Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving collaborative machine learning. However, the challenge of data imbalance, exacerbated by the non-IID nature of distributed datasets, significantly impacts model performance and fairness in FL systems. This paper investigates the implementation and evaluation of simple resampling techniques to address data imbalance within the FL framework. Using the MIMIC-III healthcare dataset, a simulated FL environment with ten virtual clients was created to test various resampling methods: SMOTE, random undersampling, and a hybrid approach. The study employed logistic regression models and evaluated performance using common and novel FL-specific metrics. Experimental results demonstrate that the hybrid resampling technique significantly outperforms other methods, improving the F1-score by 13.1% and reducing communication rounds by 25.3%. Statistical analyses, including repeated measures ANOVA and hierarchical linear modeling, confirm the robustness of these findings across varied client data distributions. This research provides a replicable framework for addressing data imbalance in FL, contributing to enhanced model fairness and efficiency in privacy-sensitive applications.

Federated Reinforcement Learning for Collaborative Intelligence in UAV-Assisted C-V2X Communications

This paper applies federated reinforcement learning (FRL) in cellular vehicle-to-everything (C-V2X) communication to enable vehicles to learn communication parameters in collaboration with a parameter server that is embedded in an unmanned aerial vehicle (UAV). Different sensors in vehicles capture different types of data, contributing to data heterogeneity. C-V2X communication networks impose additional communication overhead in order to converge to a global model when the sensor data are not independent-and-identically-distributed (non-i.i.d.). Consequently, the training time for local model updates also varies considerably. Using FRL, we accelerated this convergence by minimizing communication rounds, and we delayed it by exploring the correlation between the data captured by various vehicles in subsequent time steps. Additionally, as UAVs have limited battery power, processing of the collected information locally at the vehicles and then transmitting the model hyper-parameters to the UAVs can optimize the available power consumption pattern. The proposed FRL algorithm updates the global model through adaptive weighing of Q-values at each training round. By measuring the local gradients at the vehicle and the global gradient at the UAV, the contribution of the local models is determined. We quantify these Q-values using nonlinear mappings to reinforce positive rewards such that the contribution of local models is dynamically measured. Moreover, minimizing the number of communication rounds between the UAVs and vehicles is investigated as a viable approach for minimizing delay. A performance evaluation revealed that the FRL approach can yield up to a 40% reduction in the number of communication rounds between vehicles and UAVs when compared to gross data offloading.

TOPCOAT: towards practical two-party Crystals-Dilithium

The development of threshold protocols based on lattice-signature schemes has been of increasing interest in the past several years. The main research focus has been towards protocols constructed for various variants of Crystals-Dilithium, future NIST digital signature standard known as ML-DSA. In this work, we propose TOPCOAT, a two-party lattice-based signature algorithm that embodies Dilithium’s compression techniques. The aforesaid result is achieved by introducing a new hinting mechanism that allows parties to collaboratively calculate HighBits\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf {HighBits}$$\\end{document}. Our hinting mechanism allows public key compression similar to Dilithium. Additionally, we suggest an optimization technique to minimize number of restarts both parties need to produce a valid signature. Our approach allows to produce ≈10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx 10$$\\end{document} KB signatures within 3 rounds of communication. We prove security of our scheme under MLWE and MSIS assumptions in ROM, and provide implementation of our proposed scheme. As additional contribution, we present vulnerabilities and inconsistencies found in Liu et al. work (Future Generation Computer Systems 2023) which aimed to construct distributed lattice-based signature protocol.

Federated quantum long short-term memory (FedQLSTM)

Quantum federated learning (QFL) can facilitate collaborative learning across multiple clients using quantum machine learning (QML) models, while preserving data privacy. Although recent advances in QFL span different tasks like classification while leveraging several data types, no prior work has focused on developing a QFL framework that utilizes temporal data to approximate functions useful to analyze the performance of distributed quantum sensing networks. In this paper, a novel QFL framework that is the first to integrate quantum long short-term memory (QLSTM) models with temporal data is proposed. The proposed federated QLSTM (FedQLSTM) framework is exploited for performing the task of function approximation. In this regard, three key use cases are presented: Bessel function approximation, sinusoidal delayed quantum feedback control function approximation, and Struve function approximation. Simulation results confirm that, for all considered use cases, the proposed FedQLSTM framework achieves a faster convergence rate under one local training epoch, minimizing the overall computations, and saving 25–33% of the number of communication rounds needed until convergence compared to an FL framework with classical LSTM models.