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.
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.
This paper proposes 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 that significantly improves accuracy and convergence under diverse non-IID settings.
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.
Experimental results on real-world energy consumption datasets demonstrate that the proposed FL framework achieves competitive forecasting accuracy while preserving client data privacy, and a rigorous comparative analysis reveals that FedProx and FedTrimmedAvg consistently outperform FedAvg under non-IID conditions.