DA34FL: a robust dynamic accumulator-based authentication and key agreement with preserving model training data integrity for federated learning

Abstract

Abstract Federated Learning (FL) offers a privacy-preserving distributed learning paradigm by enabling model training without direct access to raw data. However, FL remains vulnerable to unauthorized access during training and client-server exchanges. Authentication and key agreement are essential to restrict access to legitimate participants. Existing FL authentication schemes are prone to impersonation risks, centralized PKI fragility, and insufficient integrity guarantees. To address these challenges, we propose DA $$^3$$ 4FL, a robust dynamic accumulator-based authentication and key agreement with preserving data integrity for FL. Specifically, our proposed DA $$^3$$ 4FL is an efficient authentication protocol utilizing dynamic accumulators, blockchain technology, and message authentication codes, which ensures robust member management, authorized access, and data integrity. Security analysis against the eCK adversary model confirms the resilience of our protocol. Furthermore, experiments and performance evaluations show the effectiveness of our method, with computational overhead competitive with current state-of-the-art (SOTA) baselines.