zkFL: Zero-Knowledge Proof-Based Gradient Aggregation for Federated Learning
Zhipeng Wang, Nanqing Dong, Jiahao Sun, William J. Knottenbelt, Yike Guo · IEEE Transactions on Big Data · 2024
Federated learning (FL) is a machine learning paradigm, which enables multiple and decentralized clients to collaboratively train a model under the orchestration of a central aggregator. FL can be a scalable machine learning solution inbig datascenarios. Traditional FL relies on the trust assumption of the central aggregator, which forms cohorts of clients honestly. However, a malicious aggregator, in reality, could abandon and replace the client's training models, or insert fake clients, to manipulate the final training results. In this work, we introducezkFL, which leverages zero-knowledge proofs to tackle the issue of a malicious aggregator during the training model aggregation process. To guarantee the correct aggregation results, the aggregator provides a proof per round, demonstrating to the clients that the aggregator executes the intended behavior faithfully. To further reduce the verification cost of clients, we use blockchain to handle the proof in a zero-knowledge way, where miners (i.e., the participants validating and maintaining the blockchain data) can verify the proof without knowing the clients' local and aggregated models. The theoretical analysis and empirical results show thatzkFLachieves better security and privacy than traditional FL, without modifying the underlying FL network structure or heavily compromising the training speed.