FedProto: Federated Prototype Learning across Heterogeneous Clients

Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occurs in the gradient space. Moreover, we provide a theoretical analysis to the convergence rate of FedProto under non-convex objectives.

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• 1 For example, clients may differ in terms of data distribution, network latency, input/output space, and/or model architecture, which can easily lead to the misalignment of their local gradients.
• 2 To improve the tolerance to heterogeneity, we propose a novel federated prototype learning (FedProto) framework in which the clients and server communicate the abstract class prototypes instead of the gradients.
• 3 FedProto aggregates the local prototypes collected from different clients, and then sends the global prototypes back to all clients to regularize the training of local models.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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