Jul 2026· 2026 International Conference on Emerging Trends in Information, Communication & Systems (ICETICS)· pp. 1-4· 0 citations· 23 references
Abstract
Federated learning is a decentralised machine-learning approach in which several clients jointly build a shared model without moving their raw data to one location. Rising concerns around privacy, tightening regulation, and restrictions on how data may be owned or shared have made this approach increasingly attractive in practice. Although federated learning lowers privacy exposure relative to centralised training, deploying it in practice is complicated by clients whose data are unevenly distributed and non-identically distributed, by clients that participate inconsistently, and by training that can converge unpredictably. To obtain global models that train reliably and consistently even when client data are heterogeneous, this work puts forward a federated learning system built around privacy preservation. The design follows a client–server pattern in which a coordinating server aggregates updates from local models using weights that account for imbalance among participants. The behaviour of the resulting system is examined methodically across several data-distribution regimes — IID, mildly non-IID, and severely non-IID. The experiments show that the framework converges reliably and delivers predictive accuracy that holds up well, especially in the more difficult non-IID cases. Compared with conventional federated learning baselines, the approach shows greater robustness and steadier performance across successive training rounds. Because it is simple to implement, repeatable, and built with real deployment in mind, the architecture suits privacy-sensitive, decentralised use cases such as distributed intelligent systems, industrial monitoring, and healthcare analytics.
This study implemented a comprehensive experimental framework for analysing FL performance using standard FL aggregation protocols FedAvg, FedProx, and SCAFFOLD in conjunction with Differential Privacy mechanisms; specifically, the Gaussian noise mechanism with Rényi Differential Privacy (RDP) accountants.
Training machine learning models across many institutions without moving raw data is attractive in theory, but the standard Federated Learning setup hands enormous power to one central aggregation server that participants must trust blindly. what happens when that trust assumption is violated and build a system, that does not require it. couples a four-layer distributed architecture with three stacked privacy mechanisms Local Differential Privacy to bound what any single gradient reveals, Secure Multi-Party Computation via the SecAgg protocol so the aggregation server never sees individual updates, and Tendermint-BFT blockchain consensus to create a tamper-evident audit log that no single party controls. Our main technical contribution is a formal proof that the combination of LDP and SecAgg satisfies end-to-end $(\varepsilon, \delta)$-differential privacy, including the SecAgg masking step cannot degrade the DP guarantee already earned by LDP noise injection. On CIFAR-10 with a ResNet-18 under three Dirichlet non-IID splits tested on real cluster hardware, reached 78.3% accuracy at $\varepsilon=0.83$ against six baselines including FedProx and SCAFFOLD-two standard non-IID benchmarks that prior blockchain FL work has consistently omitted.
Raman Dubey, A. Jain, Richa Sharma· International Conference on...· 0 citations
Federated Learning (FL) trains shared models across distributed clients without pooling raw data, but its behaviour under heterogeneous data distributions and the associated privacy risks remain imperfectly understood. This paper presents a controlled empirical comparison of Federated Averaging (FedAvg), Federated Proximal (FedProx), Federated Adaptive Moment Estimation (FedAdam), and Differentially Private Federated Averaging (DP-FedAvg) under matched conditions. The experiments use CIFAR-10 and MNIST with Non-Independent and Identically Distributed (non-IID) client partitions, including Dirichlet and class-restricted partitioning. Each condition is repeated across 20 independent seeds. We examine how optimisation choice shapes convergence and stability, how differential privacy affects classification performance, and how privacy exposure changes over the course of training. We find that FedAdam improves performance under severe client heterogeneity, where client gradients conflict strongly. FedProx changes the training trajectory, but we do not find it to improve cross-seed stability. DP-FedAvg reduces the success of gradient-reconstruction and membership-inference attacks, but this protection comes with a measurable reduction in classification performance due to gradient clipping and additive noise. A key finding is that privacy exposure is temporally concentrated rather than uniform across training. It is highest in the earliest rounds, when gradients are largest and most informative, and declines as training progresses. These results suggest that private FL systems should account for training dynamics, with particular emphasis on protection during early training rounds.
Imowo J. Enang, Joffrey L. Leevy, Preston Billion-Polak et al.· International Conference on...· 0 citations
Federated learning (FL) is an emerging distributed machine learning framework that enables collaborative learning among multiple parties while preserving data privacy. However, the complexity of environments and node heterogeneity in the real world result in uneven data distribution across nodes, leading to Non-IID (Non-Independent and Identically Distributed) characteristics in data distribution. Such data distribution significantly reduces the convergence and performance of the model, becoming one of the fundamental challenges in federated learning mechanisms. To address the above issue, this paper proposes a novel FL framework-FedGKD. For the Non-IID client data distribution problem, we employ client-side local data augmentation, where GAN models are deployed on each client to generate synthetic samples so that local data distribution imbalance can be effectively alleviated. To further overcome the limitations of client-side local data augmentation under Non-IID, FedGKD introduces server-side privacy-preserving data-free knowledge distillation, which can transfer the knowledge of selected clients to the server while ensuring privacy protection, further mitigating the impact of Non-IID on federated learning and solving the problem of model performance degradation caused by direct aggregation. Extensive experiments demonstrate that FedGKD significantly outperforms the baseline algorithms in terms of accuracy, while exhibiting excellent performance in other metrics.
Furui Zhan, Ziyu Deng, Yingxin Liu et al.· IEEE Transactions on Informa...· 0 citations
Federated Learning (FL) has emerged as a revolutionary paradigm in distributed machine learning, enabling multiple decentralized clients to collaboratively train models without sharing their local raw data. Despite its inherent privacy-centric design, FL remains vulnerable to sophisticated privacy attacks, such as gradient leakage and membership inference, which can reconstruct sensitive user data from communicated model updates. In order to reduce these vulnerabilities, we integrate privacy-preserving mechanisms most notably Differential Privacy (DP) and Cryptographic Protocols into the training procedure. These privacy constraints, however, come with utility loss and convergence slowdown thus highlighting a basic conflict between (differential) privacy on one side and high-order model accuracy and efficiency at another. In our paper, we carefully examine how to use convex optimization methods systematically in terms of performing this rich multi-dimensional trade-off. We center around the rigorous implementation of privacy-preserving FL couched as a bounded convex optimization task, studying how traditional and state-of-the-art optimization algorithms retain strong convergence rates even under durable privacy constraints. We benchmark the performance of these primary optimization frameworks, such as FedAvg, FedProx, and Accelerated Gradient Methods, when adopted on different privacy budgets. Theoretically, we analyze the impact of differential privacy on gradient variance in algorithms and experimentally validate how adaptive optimization (Specifically by AMSGrad) and proximal regularization can account for this noise-induced increase to enable faster convergence with a tight guarantee of differential privacy. To summarize, this work provides a unified approach for aiding the design of state-of-the-art privacy-preserving distributed learning systems that are also utility-optimal and is an important step towards using such approaches in high-stakes domains like healthcare or finance.
A. M., Nitish Kumar· International Journal of Mat...· 0 citations
An in-depth analysis of federated learning methods and paying special attention to the issue of privacy is provided, which examines new developments, concerns and tradeoffs connected with privacy, effectiveness of communication, model noise, and scalability of systems.
Aarav Mehta· International Journal of App...· 0 citations