Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Privacy-Preserving Technologies in Data
Abstract
This paper investigates the challenges and opportunities presented by federated learning in dynamic environments. Traditional federated learning approaches often rely on fixed network topology and data distribution, leading to suboptimal model performance and inefficient data utilization. We propose a novel self-adaptive federated learning algorithm that dynamically adjusts model parameters and data sharing strategies based on network topology and data distribution, addressing these limitations. The core mechanism involves a dynamically evolving network structure and a sophisticated adaptive weighting scheme. This framework aims to improve model generalization, reduce communication costs, and enhance data privacy. We present a rigorous theoretical analysis and demonstrate the effectiveness of the proposed algorithm through simulations and experiments. The key innovations lie in the algorithm's ability to continuously re-evaluate the network topology and data distribution, adapting to real-time changes.
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