An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging
This addresses privacy and security issues for participants in federated learning systems, offering a scalable and verifiable solution, though it is incremental in improving existing methods.
The paper tackles the challenges of privacy and correctness in federated learning by proposing a scalable protocol for distributed averaging that uses correlated and independent Gaussian noise, achieving differential privacy and nearly matching the utility of the trusted curator model with logarithmic communication per honest party, and includes a verification protocol for correctness using cryptographic primitives.
Learning from data owned by several parties, as in federated learning, raises challenges regarding the privacy guarantees provided to participants and the correctness of the computation in the presence of malicious parties. We tackle these challenges in the context of distributed averaging, an essential building block of federated learning algorithms. Our first contribution is a scalable protocol in which participants exchange correlated Gaussian noise along the edges of a network graph, complemented by independent noise added by each party. We analyze the differential privacy guarantees of our protocol and the impact of the graph topology under colluding malicious parties, showing that we can nearly match the utility of the trusted curator model even when each honest party communicates with only a logarithmic number of other parties chosen at random. This is in contrast with protocols in the local model of privacy (with lower utility) or based on secure aggregation (where all pairs of users need to exchange messages). Our second contribution enables users to prove the correctness of their computations without compromising the efficiency and privacy guarantees of the protocol. Our verification protocol relies on standard cryptographic primitives like commitment schemes and zero knowledge proofs.