Federated learning for sustainable IoT: Decentralized intelligence and energy-efficient applications

In this paper, the convergence time of federated learning (FL), when deployed over a realistic wireless network, is studied. In particular, with the considered model, wireless users transmit their local FL models (trained using their locally collected data) to a base station (BS). The BS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all users. Due to the limited number of resource blocks (RBs) in a wireless network, only a subset of users can be selected and transmit their local FL model parameters to the BS at each learning step. Meanwhile, since each user has unique training data samples and the BS must wait to receive all users' local FL models to generate the global FL model, the FL performance and convergence time will be significantly affected by the user selection scheme. In consequence, it is necessary to design an appropriate user selection scheme that enables all users to execute an FL scheme and efficiently train it. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize the FL convergence time while optimizing the FL performance. To address this problem, a probabilistic user selection scheme is proposed using which the BS will connect to the users, whose local FL models have large effects on its global FL model, with high probabilities. Given the user selection policy, the uplink RB allocation can be determined. To further reduce the FL convergence time, artificial neural networks (ANNs) are used to estimate the local FL models of the users that are not allocated any RBs for local FL model transmission, which enables the BS to include more users' local FL models to generate the global FL model so as to improve the FL convergence speed and performance. Simulation results show that the proposed ANN-based FL scheme can reduce the FL convergence time by up to 53.8%, compared to a standard FL algorithm.

In this paper, the convergence time of federated learning (FL), when deployed over a realistic wireless network, is studied. In particular, a wireless network is considered in which wireless users transmit their local FL models (trained using their locally collected data) to a base station (BS). The BS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all users. Due to the limited number of resource blocks (RBs) in a wireless network, only a subset of users can be selected to transmit their local FL model parameters to the BS at each learning step. Moreover, since each user has unique training data samples, the BS prefers to include all local user FL models to generate a converged global FL model. Hence, the FL training loss and convergence time will be significantly affected by the user selection scheme. Therefore, it is necessary to design an appropriate user selection scheme that can select the users who can contribute toward improving the FL convergence speed more frequently. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize the FL convergence time and the FL training loss. To solve this problem, a probabilistic user selection scheme is proposed such that the BS is connected to the users whose local FL models have significant effects on the global FL model with high probabilities. Given the user selection policy, the uplink RB allocation can be determined. To further reduce the FL convergence time, artificial neural networks (ANNs) are used to estimate the local FL models of the users that are not allocated any RBs for local FL model transmission at each given learning step, which enables the BS to improve the global model, the FL convergence speed, and the training loss. Simulation results show that the proposed approach can reduce the FL convergence time by up to 56% and improve the accuracy of identifying handwritten digits by up to 3%, compared to a standard FL algorithm.

Privacy Preserving Technology using Federated Learning and Blockchain in protecting against Adversarial Attacks for Retinal Imaging

In this paper, the problem of training federated learning (FL) algorithms over a wireless network with mobile users is studied. In the considered model, several mobile users and a network base station (BS) cooperatively perform an FL algorithm. In particular, the wireless mobile users train their local FL models and send the trained local FL model parameters to the BS. The BS will then integrate the received local FL models to generate a global FL model and send it back to all users. Due to the limited training time at each iteration, the number of users that can transmit their local FL models to the BS will be affected by changes in the users' locations and wireless channels. In this paper, this joint learning, user selection, and wireless resource allocation problem is formulated as an optimization problem whose goal is to minimize the FL loss function, which captures the FL performance, while meeting the transmission delay requirement. To solve this problem, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of the users' mobility and wireless factors on FL. Then, based on the expected FL convergence rate, the user selection and uplink resource allocation is optimized at each FL iteration so as to minimize the FL loss function while satisfying the FL parameter transmission delay requirement. Simulation results show that the proposed approach can reduce the FL loss function value by up to 20% compared to a standard FL algorithm.

With the requirements of government data protection regulations and industrial concerns regarding data protection and privacy, the security level required for data privacy and protection has increased. This has led researchers to investigate techniques that can train cybersecurity machine learning (ML) models without sharing personal data. Federated Learning (FL) is a newly developed decentralized and distributed ML mechanism that emphasize privacy. In this technique, a learning algorithm is trained without collecting or exchanging sensitive data from distributed client models running at different locations. With the rapid increase in the number of cybersecurity attacks reported in the aviation industry in the last two decades, strong, dynamic, and effective countermeasures are required to protect the aviation industry and air passengers against such attacks, which can most of the time lead to catastrophic situations. This paper proposes and implements an FL model for identifying cyberattacks on a Software Defined Network (SDN)-based aeronautical communication networks. The machine learning model used in the FL architecture is a Deep Neural Network (DNN) model. The publicly available National Security Laboratory–Knowledge Discovery and Datamining (NSL-KDD) dataset was employed to train and validate the proposed FL model. The simulation results illustrated that the FL-based system can accurately and effectively identify potential cybersecurity attacks and minimize the risk of data and service exposure without degrading model performance. A comparison was also made between the FL and non-FL machine learning models. Preliminary results demonstrated that the FL model outperformed the non-FL machine learning approaches. FL reached an accuracy of 96%, compared to 76% and 83% for NFL.

In this article, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that generates a global FL model and sends the model back to the users. Since all training parameters are transmitted over wireless links, the quality of training is affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS needs to select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To seek the solution, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can improve the identification accuracy by up to 1.4%, 3.5% and 4.1%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation, 2) a standard FL algorithm with random user selection and resource allocation, and 3) a wireless optimization algorithm that minimizes the sum packet error rates of all users while being agnostic to the FL parameters.

In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users perform an FL algorithm that trains their local FL models using their own data and send the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL learning algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To address this problem, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can reduce the FL loss function value by up to 10% and 16%, respectively, compared to 1) an optimal user selection algorithm with random resource allocation and 2) a random user selection and resource allocation algorithm.

The past decade has seen substantial growth in the prevalence and capabilities of wearable devices. For instance, recent human activity recognition (HAR) research has explored using wearable devices in applications such as remote monitoring of patients, detection of gait abnormalities, and cognitive disease identification. However, data collection poses a major challenge in developing HAR systems, especially because of the need to store data at a central location. This raises privacy concerns and makes continuous data collection difficult and expensive due to the high cost of transferring data from a user’s wearable device to a central repository. Considering this, we explore the adoption of federated learning (FL) as a potential solution to address the privacy and cost issues associated with data collection in HAR. More specifically, we investigate the performance and behavioral differences between FL and deep learning (DL) HAR models, under various conditions relevant to real-world deployments. Namely, we explore the differences between the two types of models when (i) using data from different sensor placements, (ii) having access to users with data from heterogeneous sensor placements, (iii) considering bandwidth efficiency, and (iv) dealing with data with incorrect labels. Our results show that FL models suffer from a consistent performance deficit in comparison to their DL counterparts, but achieve these results with much better bandwidth efficiency. Furthermore, we observe that FL models exhibit very similar responses to those of DL models when exposed to data from heterogeneous sensor placements. Finally, we show that the FL models are more robust to data with incorrect labels than their centralized DL counterparts.

FederatedTrust: A solution for trustworthy federated learning

• We propose an FL model to predict a client’s (or loan requester’s) financial situation by considering variant local epochs for the data holders of the clients (e.g., banks, financial organizations). • We leverage FL strategy that consider customer’s local resources to assign computational task during training. Particularly, the local computational tasks of each FL client is assigned based on their data volume, bandwidth, and network availability. • We analyze our prediction model by considering various batch sizes and client numbers for the training phase. • To the end, we visualize the performance of our FL model comparing with a centralized model, and also with a mean local model, and the best local model in an FL process. In recent years, as economic stability is shaking, and the unemployment rate is growing high due to the COVID-19 effect, assigning credit scoring by predicting consumers’ financial conditions has become more crucial. The conventional machine learning (ML) and deep learning approaches need to share customer’s sensitive information with an external credit bureau to generate a prediction model that opens up the door of privacy leakage. A recently invented privacy-preserving distributed ML scheme referred to as Federated learning (FL) enables generating a target model without sharing local information through on-device model training on edge resources. In this paper, we propose an FL-based application to predict customers’ financial issues by constructing a global learning model that is evolved based on the local models of the distributed agents. The local models are generated by the network agents using their on-device data and local resources. We used the FL concept because the learning strategy does not require sharing any data with the server or any other agent that ensures the preservation of customers’ sensitive data. To that end, we enable partial works from the weak agents that eliminate the issue if the model convergence is retarded due to straggler agents. We also leverage asynchronous FL that cut off the extra waiting time during global model generation. We simulated the performance of our FL model considering a popular dataset, Give me Some Credit (Freshcorn, 2017). We evaluated our proposed method considering a a different number of stragglers and setting up various computational tasks (e.g., local epoch, batch size), and simulated the training loss and testing accuracy of the prediction model. Finally, we compared the F1-score of our proposed model with the existing centralized and decentralized approaches. Our results show that our proposed model achieves an almost identical F1-score as like centralized model even when we set up a skew-level of more than 80 % and outperforms the state-of-the-art FL models by obtaining an average of 5 ∼ 6 % higher accuracy when we have resource-constrained agents within a learning environment.

In this paper, the performance optimization of federated learning (FL), when deployed over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp) is studied. In particular, a MIMO system is considered in which edge devices transmit their local FL models (trained using their locally collected data) to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. The PS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all devices. Due to the limited bandwidth in a wireless network, AirComp is adopted to enable efficient wireless data aggregation. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To tackle this challenge, we propose a modified federated averaging (FedAvg) algorithm that combines digital modulation with AirComp to mitigate wireless fading while ensuring the communication efficiency. This is achieved by a joint transmit and receive beamforming design, which is formulated as an optimization problem to dynamically adjust the beamforming matrices based on current FL model parameters so as to minimize the transmitting error and ensure the FL performance. To achieve this goal, we first analytically characterize how the beamforming matrices affect the performance of the FedAvg in different iterations. Based on this relationship, an artificial neural network (ANN) is used to estimate the local FL models of all devices and adjust the beamforming matrices at the PS for future model transmission. The algorithmic advantages and improved performance of the proposed methodologies are demonstrated through extensive numerical experiments.

Deploying federated learning (FL) in edge clouds poses challenges, especially when multiple models are concurrently trained in resource-constrained edge environments. Existing research on federated edge learning has predominantly focused on client selection for training a single FL model, typically with a fixed learning topology. Preliminary experiments indicate that FL models with adaptable topologies exhibit lower learning costs compared to those with fixed topologies. This paper delves into the intricacies of jointly selecting participants and learning topologies for multiple FL models simultaneously trained in the edge cloud. The problem is formulated as an integer non-linear programming problem, aiming to minimize total learning costs associated with all FL models while adhering to edge resource constraints. To tackle this challenging optimization problem, we introduce a two-stage algorithm that decouples the original problem into two sub-problems and iteratively addresses them separately with efficient heuristics. Our method enhances resource competition and load balancing in edge clouds by allowing FL models to choose participants and learning topologies independently. Extensive experiments conducted with real-world networks and FL datasets affirm the better performance of our algorithm, demonstrating lower average total costs with up to 33.5% and 39.6% compared to previous methods designed for multi-model FL.

In this work, we present a blockchain-based federated learning (FL) framework that aims achieving high system efficiency while simultaneously addressing issues relating to data sparsity and the disclosure of private information. It is more efficient to build a number of smaller clusters rather than one big cluster for multiple networks. Blockchain-based FL is carried out in each cluster, with the model changes being compiled at the end of the process. Following that, the accumulated updates are swapped across the clusters, which, in practise, improves the updates that are accessible for each cluster. When compared to the extensive interactions that take place in blockchain-based FL, cluster-based FL only sends a limited number of aggregated updates across a substantial distance. This is in contrast to the extensive interactions that take place in blockchain-based FL. In order to conduct an analysis of our system, we have implemented the prototypes of both cluster and blockchain-based FL models. The findings of the experiments show that cluster-based FL model raise the accuracy goes upto 72.6%, and goes down to 11%. The loss goes upto 3.6 and goes down to 0.8. In addition, cluster-based FL model has the potential to hasten the convergence of the model, provided that the same quantity of data is input into it. The reason for this is due to the fact that during a training cycle, cluster-based FL model combines the computational resources of many different clusters.

Federated learning (FL) has recently emerged as a promising distributed machine learning (ML) paradigm. Practical needs of the right to be forgotten and countering data poisoning attacks call for efficient techniques that can remove, or unlearn, specific training data from the trained FL model. Existing unlearning techniques in the context of ML, however, are no longer in effect for FL, mainly due to the inherent distinction in the way how FL and ML learn from data. Therefore, how to enable efficient data removal from FL models remains largely under-explored. In this paper, we take the first step to fill this gap by presenting FedEraser, the first federated unlearning method-ology that can eliminate the influence of a federated client’s data on the global FL model while significantly reducing the time used for constructing the unlearned FL model. The basic idea of FedEraser is to trade the central server’s storage for unlearned model’s construction time, where FedEraser reconstructs the unlearned model by leveraging the historical parameter updates of federated clients that have been retained at the central server during the training process of FL. A novel calibration method is further developed to calibrate the retained updates, which are further used to promptly construct the unlearned model, yielding a significant speed-up to the reconstruction of the unlearned model while maintaining the model efficacy. Experiments on four realistic datasets demonstrate the effectiveness of FedEraser, with an expected speed-up of 4× compared with retraining from the scratch. We envision our work as an early step in FL towards compliance with legal and ethical criteria in a fair and transparent manner.

Federated learning (FL) enables users to train the global model cooperatively without exposing their private data across the engaged parties, which is widely used in privacy-sensitive business. However, during the life cycle of FL models, both adversaries’ attacks and ownership generalization threaten the FL models’ copyright and affect the models’ reliability. To address these problems, existing model watermarking techniques can be used to verify FL model’s ownership. However, due to the lack of credible binding from “model extracted watermarks” to “ownership verification”, it is difficult to form a closed-loop watermarking framework for copyright protection. Therefore, starting from the shortcomings of the current watermark verification scheme, this article proposed WFB, a blockchain-empowered watermarking framework for ownership verification of federated models. Firstly, we propose a improved watermark generation algorithm to solve the credibility issue of watermarks. Secondly, we propose a watermark embedding method in federated learning, while blockchain technology is used to ensure the credible storage of watermark information throughout the process. Thirdly, the credibility of ownership verification is improved because of the watermark authenticity. Experimental results demonstrate the fidelity, effectiveness and robustness of WFB, with other superiorities such as improving process security and traceability.