A Verifiable Chained Federated Learning Framework with Distance-Based Grouped Mechanism

In federated learning, multiple clients collaborate to train a global model without exchanging raw data, which addresses issues of data silos and the leakage of data privacy. However, existing federated learning schemes often suffer from high communication overhead and unreliable server-side aggregation. To address these limitations, this paper proposes a verifiable chained federated learning mechanism with Euclidean distance-based grouping, termed VDCG-FL. Grouping is used to improve communication efficiency, while verification ensures the accuracy of aggregated results. Unlike conventional approaches, VDCG-FL groups clients according to their Euclidean distance to the server, thereby reducing communication latency, avoiding long-distance transmissions, and enhancing the stability of model aggregation. Moreover, Lagrange interpolation is used for verification to ensure aggregation correctness while incurring significantly lower computational overhead than traditional cryptographic methods. Extensive experiments demonstrate that VDCG-FL improves aggregation stability under non-IID data distributions while simultaneously reducing communication overhead.