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Learning from the collective knowledge of data dispersed across privatesources can provide neural networks with enhanced generalization capabilities.Federated learning, a method for collaboratively training a machine learningmodel across remote clients, achieves this by combining client models via theorchestration of a central server. However, current approaches face twocritical limitations: i) they struggle to converge when client domains aresufficiently different, and ii) current aggregation techniques produce anidentical global model for each client. In this work, we address these issuesby reformulating the typical federated learning setup: rather than learning asingle global model, we learn N models each optimized for a common objective.To achieve this, we apply a weighted distance minimization to model parametersshared in a peer-to-peer topology. The resulting framework, Iterative ParameterAlignment, applies naturally to the cross-silo setting, and has the followingproperties: (i) a unique solution for each participant, with the option toglobally converge each model in the federation, and (ii) an optionalearly-stopping mechanism to elicit fairness among peers in collaborativelearning settings. These characteristics jointly provide a flexible newframework for iteratively learning from peer models trained on disparatedatasets. We find that the technique achieves competitive results on a varietyof data partitions compared to state-of-the-art approaches. Further, we showthat the method is robust to divergent domains (i.e. disjoint classes acrosspeers) where existing approaches struggle.

Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter Alignment