TL;DR
Using the AzurePublicDatasetV2 and Alibaba datasets a novel multi-horizon hybrid neural architecture integrating Gated Recurrent Units (GRUs), multi-head attention mechanisms, and Kalman filter is proposed to offer client’s privacy and is integrated with client-side differential privacy.
Overview: Federated Learning (FL) is a type of machine learning in which multiple clients use their own data to train a model. This does not require sending the raw data from each client to a central server. However, FL is currently very vulnerable to several types of attacks, including inference, model inversion, and membership inference. In this study, we have implemented a comprehensive experimental framework for analysing FL performance using standard FL aggregation protocols FedAvg, FedProx, and SCAFFOLD in conjunction with Differential Privacy (DP) mechanisms; specifically, the Gaussian noise mechanism with Rényi Differential Privacy (RDP) accountants. Our experiments all used K = 100 simulated clients, with heterogeneous data distribution across the entire federation (non-IID), and the respective clean datasets used were MNIST and CIFAR-10. For example, in our IID scenarios, our DP-FedAvg protocol achieved an accuracy of 84.9% on MNIST with ε = 1.0 and δ = 10^-5 (i.e., no privacy guarantees and 92.6% reuse of the same parameters). This corresponds to a 7.7-percentage-point trade-off in accuracy for provable privacy. In our non-IID scenario, we achieved an accuracy of 72.8% for the exact same privacy parameters and an LDA αᴸᴸᴸ = 0.5. We systematically analyse how various combinations of clipping norms on gradients, noise multipliers, and client participation rates converged. All results have been reported as-is, along with the associated standard deviation across each of the five independent sample runs that we performed using different random seed values. Finally, our framework will assist practitioners deploying privacy-friendly FL within either healthcare, banking, or Internet of Things environments.
: Federated learning (FL) enables collaborative training of deep neural architectures while preserving data privacy, yet its performance often deteriorates in non-IID scenarios, which stems from client-side distribution drift and divergent local updates induced by pervasive data heterogeneity. This challenge is particularly critical for maintaining the structural consistency and generalization of neural models across diverse, distributed sources with significant distribution shifts. In this paper, we investigate how to effectively mitigate label distribution shift and feature distribution skew to enhance the global representation stability of neural architectures. We propose Federated Learning with Consistency Optimization Algorithms (FedCO), a novel optimization framework that incorporates a label-skew-aware correction loss and neural feature distribution regularization during local training. Specifically, our method aligns the internal statistics of architectural components between local and global models to suppress feature-space drift. Combined with an adaptive global aggregation mechanism guided by label entropy, this approach ensures that model updates from heterogeneous clients are consistently integrated into the global architectural parameters. Experimental results on multiple benchmarks demonstrate that FedCO significantly improves accuracy and convergence under diverse non-IID settings. For instance, on CIFAR-10 with extreme heterogeneity ( α = 0.05), FedCO achieves 73.64% accuracy, outperforming FedAvg by 7.32%; it also attains the highest accuracies on CIFAR-100 (62.63%) and TinyIma-geNet (37.82%) under the same setting. Our findings provide a robust strategy for integrating distribution-aware local training with adaptive structural aggregation, offering new insights into enhancing the reliability of distributed neural systems in real-world deployments. The code related to this algorithm is available at https://github.com/Donglin0730/ FedCO.
A federated learning, which is merged with GNN to allow decentralised training whilst maintaining the relational structure of data in terms of interaction, is proposed, which demonstrates the usefulness of federated graph-based learning in order to have secure and accurate recommendation systems.
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.
The fast growth of big data, along with the growing stricter privacy laws, poses tremendous challenges to the traditional centralized implementation of AI models. Federated Learning (FL) offers a decentralized solution; however, it is undermined by a number of performance limitations such as large communicational overhead, statistical heterogeneity due to nonindependent and identically distributed data, and small computational capability of edge clients. This paper presents an optimized AI-based FL architecture, which consolidates a client selection algorithm (that is based on reinforcement-learning) and a dynamic and fairness-conscious aggregation protocol, as well as a new query-fragment caching solution. The combined model is strategically focusing on high utility players, reducing skew in data during model aggregation, and lessening on-device unnecessary processing. The empirical analysis of CIFAR-10 and FEMNIST shows that the presented framework reduces the communication rounds by 38% and the model accuracy is increased by 5.7% when compared with Fed Avg and Fed Prox, at the same time, data privacy is also rigorously maintained.
Accurate load forecasting plays a vital role in optimizing energy distribution and integrating renewable energy sources within smart grid systems. However, traditional centralized deep learning approaches present major challenges related to data privacy, communication overhead, and scalability, particularly in scenarios involving distributed energy consumers. To address these concerns, this study proposes a federated learning (FL) framework that leverages Gated Recurrent Unit (GRU) networks to enable decentralized and privacy-preserving load forecasting. The proposed approach is evaluated using three distinct aggregation strategies: Federated Averaging (FedAvg), Federated Proximal (FedProx), and Federated Averaging with Trimmed Mean (FedTrimmedAvg). These methods aim to alleviate data heterogeneity and client drift, which are prevalent in non-independent and identically distributed (non-IID) settings commonly encountered in smart grid environments—challenges known to limit the effectiveness of standard FedAvg and that motivate the need for more robust alternatives. Experimental results on real-world energy consumption datasets demonstrate that the proposed FL framework achieves competitive forecasting accuracy while preserving client data privacy. A rigorous comparative analysis reveals that FedProx and FedTrimmedAvg consistently outperform FedAvg under non-IID conditions, with FedTrimmedAvg offering the highest robustness to outliers and inconsistent client behavior. These findings highlight the effectiveness of robust aggregation techniques in federated settings and present a scalable, privacy-aware solution for intelligent energy management in next-generation smart grid infrastructures.