Enhanced Diabetes Detection via a Privacy-Preserving Federated Learning Framework

Federated learning (FL) is a promising approach to enabling collaborative model training without centralized data sharing, a crucial requirement in scientific domains where data privacy, ownership, and compliance constraints are critical. However, building user-friendly enterprise-level FL frameworks that are both scalable and privacy-preserving remains challenging, especially when bridging the gap between local prototyping and distributed deployment across heterogeneous client computing infrastructures. In this paper, based on our experiences building the Advanced Privacy-Preserving Federated Learning (APPFL) framework, we present our vision for an enterprise-grade, privacy-preserving FL framework designed to scale seamlessly across computing environments. We identify several key capabilities that such a framework must provide: (1) Scalable local simulation and prototyping to accelerate experimentation and algorithm design; (2) seamless transition from simulation to deployment; (3) distributed deployment across diverse, real-world infrastructures, from personal devices to cloud clusters and HPC systems; (4) multi-level abstractions that balance ease of use and research flexibility; and (5) comprehensive privacy and security through techniques such as differential privacy, secure aggregation, robust authentication, and confidential computing. We further discuss architectural designs to realize these goals. This framework aims to bridge the gap between research prototypes and enterprise-scale deployment, enabling scalable, reliable, and privacy-preserving AI for science.

Personalized federated learning (PFL) has garnered significant attention for its ability to address heterogeneous client data distributions while preserving data privacy. However, when local client data is limited, deep learning models often suffer from insufficient training, leading to suboptimal performance. Foundation models, such as CLIP (Contrastive Language-Image Pretraining), exhibit strong feature extraction capabilities and can alleviate this issue by fine-tuning on limited local data. Despite their potential, foundation models are rarely utilized in federated learning scenarios, and challenges related to integrating new clients remain largely unresolved. To address these challenges, we propose the Dual Prompt Personalized Federated Learning (DP2FL) framework, which introduces dual prompts and an adaptive aggregation strategy. DP2FL combines global task awareness with local data-driven insights, enabling local models to achieve effective generalization while remaining adaptable to specific data distributions. Moreover, DP2FL introduces a global model that enables prediction on new data sources and seamlessly integrates newly added clients without requiring retraining. Experimental results in highly heterogeneous environments validate the effectiveness of DP2FL’s prompt design and aggregation strategy, underscoring the advantages of prediction on novel data sources and demonstrating the seamless integration of new clients into the federated learning framework.

While Federated Learning (FL) provides a privacy-preserving approach to analyze sensitive data without centralizing training data, the field lacks an detailed comparison of emerging open-source FL frameworks. Furthermore, there is currently no standardized, weighted evaluation scheme for a fair comparison of FL frameworks that would support the selection of a suitable FL framework. This study addresses these research gaps by conducting a comparative analysis of 15 individual open-source FL frameworks filtered by two selection criteria, using the literature review methodology proposed by Webster and Watson. These framework candidates are compared using a novel scoring schema with 15 qualitative and quantitative evaluation criteria, focusing on features, interoperability, and user friendliness. The evaluation results show that the FL framework Flower outperforms its peers with an overall score of 84.75%, while Fedlearner lags behind with a total score of 24.75%. The proposed comparison suite offers valuable initial guidance for practitioners and researchers in selecting an FL framework for the design and development of FL-driven systems. In addition, the FL framework comparison suite is designed to be adaptable and extendable accommodating the inclusion of new FL frameworks and evolving requirements.

Federated Learning (FL) aims to protect data privacy by enabling clients to collectively train machine learning models without sharing their raw data. However, recent studies demonstrate that information exchanged during FL is subject to Gradient Inversion Attacks (GIA) and, consequently, a variety of privacy-preserving methods have been integrated into FL to thwart such attacks, such as Secure Multi-party Computing (SMC), Homomorphic Encryption (HE), and Differential Privacy (DP). Despite their ability to protect data privacy, these approaches inherently involve substantial privacy-utility trade-offs. By revisiting the key to privacy exposure in FL under GIA, which lies in the frequent sharing of model gradients that contain private data, we take a new perspective by designing a novel privacy preserve FL framework that effectively ``breaks the direct connection'' between the shared parameters and the local private data to defend against GIA. Specifically, we propose a Hypernetwork Federated Learning (HyperFL) framework that utilizes hypernetworks to generate the parameters of the local model and only the hypernetwork parameters are uploaded to the server for aggregation. Theoretical analyses demonstrate the convergence rate of the proposed HyperFL, while extensive experimental results show the privacy-preserving capability and comparable performance of HyperFL.

Federated Learning (FL) is a distributed learning framework that enables data sharing among multiple devices to protect data privacy. Blockchain is a decentralized ledger that can record data securely and reliably. The blockchain-based FL (BFL) framework has been used to share data and computing resources in multiple clusters. However, for the BFL framework among multi-institutional clusters, data sparsity in a cluster is a key issue. Most of the relevant works assume that the data in one cluster is rich enough to build a suitable model, which is not always satisfied in all scenarios. One method to address the problem is that enlarging the size of a BFL cluster that covers as many nodes as possible is one way. However, this method will increase communication overheads and reduce transaction throughput of the blockchain. To solve the above issues, we propose a communication-efficient blockchain-based FL framework called CCFL, which connects multiple BFL clusters to solve the data sparsity issue. We also design a pearson correlation coefficient-based dynamic model filtering mechanism that filters unnecessary models to reduce communication costs and exclude malicious models. Moreover, we illustrate a reliable contribution-based interactive validation reputation mechanism to prevent malicious nodes from participating in the training. We carry out some experiments to show the feasibility and efficiency of the proposed framework.

Wireless federated learning (FL) is a collaborative machine learning (ML) framework in which wireless client-devices independently train their ML models and send the locally trained models to the FL server for aggregation. In this paper, we consider the coexistence of privacy-sensitive client-devices and privacy-insensitive yet computing-resource constrained client-devices, and propose an FL framework with a hybrid centralized training and local training. Specifically, the privacy-sensitive client-devices perform local ML model training and send their local models to the FL server. Each privacy-insensitive client-device can have two options, i.e., (i) conducting a local training and then sending its local model to the FL server, and (ii) directly sending its local data to the FL server for the centralized training. The FL server, after collecting the data from the privacy-insensitive client-devices (which choose to upload the local data), conducts a centralized training with the received datasets. The global model is then generated by aggregating (i) the local models uploaded by the client-devices and (ii) the model trained by the FL server centrally. Focusing on this hybrid FL framework, we firstly analyze its convergence feature with respect to the client-devices' selections of local training or centralized training. We then formulate a joint optimization of client-devices' selections of the local training or centralized training, the FL training configuration (i.e., the number of the local iterations and the number of the global iterations), and the bandwidth allocations to the client-devices, with the objective of minimizing the overall latency for reaching the FL convergence. Despite the non-convexity of the joint optimization problem, we identify its layered structure and propose an efficient algorithm to solve it. Numerical results demonstrate the advantage of our proposed FL framework with the hybrid local and centralized training as well as our proposed algorithm, in comparison with several benchmark FL schemes and algorithms.

The trade-off between privacy and accuracy presents a challenge for current federated learning (FL) frameworks, hindering their progress from theory to application. The main issues with existing FL frameworks stem from a lack of interpretability and targeted privacy protections. To cope with these, we proposed Disentangled Federated Learning for Privacy (DFLP) which employes disentanglement, one of interpretability techniques, in private FL frameworks. Since sensitive properties are client-specific in nature, our main idea is to turn this feature into a tool that strikes the balance between data privacy and FL model performance, enabling the sensitive attributes to be private. DFLP disentangles the client-specific and class-invariant attributes to mask the sensitive attributes precisely. To our knowledge, this is the first work that successfully integrates disentanglement and the nature of sensitive attributes to achieve privacy protection while ensuring high FL model performance. Extensive experiments validate that disentanglement is an effective method for accuracy-aware privacy protection in FL frameworks.

A novel multi-task federated learning (FL) framework is proposed in this paper to optimize the traffic prediction models without sharing the collected data among traffic stations. In particular, a divisive hierarchical clustering is first introduced to partition the collected traffic data at each station into different clusters. The FL is then implemented to collaboratively train the learning model for each cluster of local data distributed across the stations. Using the multi-task FL framework, the route planning is studied where the road map is modeled as a time-dependent graph and a modified A * algorithm is used to determine the route with the shortest traveling time. Simulation results showcase the prediction accuracy improvement of the proposed multi-task FL framework over two baseline schemes. The simulation results also show that, when using the multi-task FL framework in the route planning, an accurate traveling time can be estimated and an effective route can be selected.

FedPCL-CDR: A federated prototype-based contrastive learning framework for privacy-preserving cross-domain recommendation.

Data security and user privacy concerns are receiving increasing attention. Federated learning models based on differential privacy offer a distributed machine learning framework that protects data privacy. However, the noise introduced by the differential privacy mechanism may affect the model’s usability, especially when reasonable gradient clipping is absent. Fluctuations in the gradients can lead to issues like gradient explosion, compromising training stability and potentially leaking privacy. Therefore, gradient clipping has become a crucial method for protecting both model performance and data privacy. To balance privacy protection and model performance, we propose the Adaptive Weight-Based Differential Privacy Federated Learning (AWDP-FL) framework, which processes model gradient parameters at the neural network layer level. First, by designing and recording the change trends of two-layer historical gradient sequences, we analyze and predict gradient variations in the current iteration and calculate the corresponding weight values. Then, based on these weights, we perform adaptive gradient clipping for each data point in each training batch, which is followed by gradient momentum updates based on the third moment. Before uploading the parameters, Gaussian noise is added to protect privacy while maintaining model accuracy. Theoretical analysis and experimental results validate the effectiveness of this framework under strong privacy constraints.

In order to use datasets collected from multiple vehicles to train a machine learning model while ensuring vehicle user privacy, federal learning framework was introduced into the Internet of Vehicles. Federated learning is a distributed learning framework. Under the federated learning framework, the packet error rate and wireless bandwidth have a great influence on the global model training process because the vehicle exchanges model parameters with the central server through the wireless channel. With limited bandwidth, the central server needs to select a more appropriate subset of vehicles candidates to participate in federated learning. In this paper, image classification is taken as a typical application in the Internet of vehicles. The dataset contents of different vehicles are different, and the selection of different subsets of vehicles will affect the accuracy and convergence rate of the global model. Therefore, an algorithm of vehicle selection and wireless resource allocation based on dataset content is proposed. Vehicles selection and wireless resource allocation are designed as an optimization problem by joint considerations of vehicle computing resource, datasets, and wireless resources with the goal of maximizing loss function decay of the global model. Finally, simulation with the CIFAR-10 dataset verifies that the vehicle selection and resource allocation algorithm based on the dataset content is superior to the baseline methods in terms of model accuracy and convergence rate.

Federated learning (FL) is a new machine learning framework that trains a joint model across a large number of decentralized computing devices. Existing methods, e.g., Federated Averaging (FedAvg), are able to provide an optimization guarantee by synchronously training the joint model, but usually suffer from stragglers, i.e., IoT devices with low computing power or communication bandwidth, especially on optimization problems with non-i.i.d distributed training data. To mitigate the influence of stragglers, this paper presents a novel FL framework, namely Hybrid Federated Learning (HFL), which consists of two major components: synchronous kernel and asynchronous updater that correspondingly addresses the normal devices and stragglers. Unlike traditional synchronous FL frameworks, HFL introduces the asynchronous updater which actively pulls unsyn-chronized and delayed local updates from stragglers to improve the efficiency and effectiveness of FL. Particularly, we propose a novel algorithm, delayed federated-SGD (DF-SGD), to merge the delayed local updates into the joint model. The results of comprehensive experiments under the HFL framework show that DF-SGD outperforms current FL methods.

Within the federated learning (FL) framework, the client collaboratively trains the model in coordination with a central server, while the training data can be kept locally on the client. Thus, the FL framework mitigates the privacy disclosure and costs related to conventional centralized machine learning. Nevertheless, current surveys indicate that FL still has problems in terms of communication efficiency and privacy risks. In this paper, to solve these problems, we develop an FL framework with communication-efficient and privacy-preserving (FLCP). To realize the FLCP, we design a novel compression algorithm with efficient communication, namely, adaptive weight compression FedAvg (AWC-FedAvg). On the basis of the non-independent and identically distributed (non-IID) and unbalanced data distribution in FL, a specific compression rate is provided for each client, and homomorphic encryption (HE) and differential privacy (DP) are integrated to provide demonstrable privacy protection and maintain the desirability of the model. Therefore, our proposed FLCP smoothly balances communication efficiency and privacy risks, and we prove its security against “honest-but-curious” servers and extreme collusion under the defined threat model. We evaluate the scheme by comparing it with state-of-the-art results on the MNIST and CIFAR-10 datasets. The results show that the FLCP performs better in terms of training efficiency and model accuracy than the baseline method.

Federated learning (FL) is widely used in privacy sensitive applications for isolated data islands, with the aim of achieving distributed model training, privacy enhancement and model sharing. Electromyographic (EMG) signals are a type of data collected from wearable sensors of subjects which are distributed on multiple devices, highly personalized and play an important role in several applications including prosthetic hand control, sign languages, grasp recognition, etc. This paper utilizes the FL method to detect single and combined finger movements based on EMG signals. The existing research on FL for wearable healthcare faces challenges of variable probability distributions of data, the need for prerequisite knowledge of server model and computational burdens in parameter transmission. To address these problems, this paper proposes a communication efficient FL framework in which each device only needs to transmit the weight matrices of local models to the server for model aggregation. To further reduce the FL transmission delay, a joint learning and resource allocation problem is formulated via optimizing transmit power of each device, time allocation, and user selection. To solve the delay minimization problem, the objective function is first converted to a tractable expression and then the difference of two convex functions programming is adopted. Simulation results using real EMG signals show that the proposed FL framework with personalized training process successfully detects single and combined finger movements for distributed users. Two public EMG datasets with 10 and 15 different finger movements are employed. Over 98% overall test accuracy is achieved in both datasets which surpasses the conventional learning framework by 1.6% and 0.5% on average. Different scenarios with regard to access points and users are investigated and the convexity of the proposed model is discussed.

Federated learning (FL), as one of novel machine learning frameworks, has been widely applied in edge computing (EC) fields. Most of the existing FL frameworks are implemented based on gradient descent (GD), and the training resources are assumed to be sufficient. However, in practical training, not all optimization problem can be well addressed by GD under FL framework in edge computing environment, and especially the edge resources are also limited. In this paper, based on the limited training resource and the FL framework, we study a kind of non-convex optimization problems, namely the gaussian mixture model (GMM), and propose an adaptive distributed expectation maximization (DEM) algorithm of GMM, which are not well addressed by using gradient descent. In particular, we analyze and give the convergence bound of the adaptive DEM. With the bound of adaptive DEM, we propose a local update control algorithm, which can achieve the best compromise between local update and global parameter aggregation of FL to achieve the maximization utilization of given resource budget. The experimental results demonstrate that adaptive DEM can well work out a kind of non-convex optimization problem under the FL framework and edge resource constrained environment.