A Graph Federated Architecture with Privacy Preserving Learning
This addresses privacy and robustness problems for federated learning systems handling sensitive data, though it appears incremental as it builds on existing privacy techniques.
The authors tackled the privacy and robustness issues in federated learning by developing a graph federated learning scheme with cryptographic and differential privacy methods, showing that under convexity and Lipschitz conditions, the privatized process matches the performance of the non-private algorithm even with increased noise variance.
Federated learning involves a central processor that works with multiple agents to find a global model. The process consists of repeatedly exchanging estimates, which results in the diffusion of information pertaining to the local private data. Such a scheme can be inconvenient when dealing with sensitive data, and therefore, there is a need for the privatization of the algorithms. Furthermore, the current architecture of a server connected to multiple clients is highly sensitive to communication failures and computational overloads at the server. Thus in this work, we develop a private multi-server federated learning scheme, which we call graph federated learning. We use cryptographic and differential privacy concepts to privatize the federated learning algorithm that we extend to the graph structure. We study the effect of privatization on the performance of the learning algorithm for general private schemes that can be modeled as additive noise. We show under convexity and Lipschitz conditions, that the privatized process matches the performance of the non-private algorithm, even when we increase the noise variance.