TL;DR
FedEntGate’s capability to significantly advance the practical Pareto frontier is demonstrated, achieving a superior balance across the three competing objectives, and the framework thus presents a secure and efficient solution for privacy-sensitive applications such as healthcare and finance.
FL-HeteroSecure consistently outperforms state-of-the-art baselines, including FedGAN, FMDS-FL, and HFMDS-FL, improving accuracy, accelerating convergence, and reducing communication costs.
Federated learning (FL) trains a shared model across data holders that cannot pool their records, but deployments remain bounded by three coupled costs: uplink traffic from repeated model exchange, accuracy loss under statistically heterogeneous clients, and the information that updates still leak. These are usually at...
The growing volume of data from smart devices offers significant potential for machine learning, yet privacy concerns hinder centralized use. Federated Learning (FL) has emerged as a promising decentralized learning (DL) approach enabling the use of distributed data without compromising privacy. However, practical depl...
This work proposes a data-Quality-aware aggregation framework by introducing an Evolutionary-computation-inspired de-sign into Federated learning ( FedEvoQ), with a lightweight dual-branch architecture.
The concept of Federated Learning (FL) allows training models in a decentralized way without distributing raw data but, nonetheless, the gradients are vulnerable to privacy attacks that include gradient inversion, reconstruction, and membership inference. Differential Privacy (DP) is broadly used to address these risks...
With the widespread adoption of interactive, mobile, and wearable technologies, an increasing amount of privacy-sensitive and statistically heterogeneous sensor data is being continuously generated by ubiquitous user devices. Federated learning (FL) enables multiple parties to jointly train a machine-learning model whi...
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