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Both centralized and decentralized approaches have shown excellentperformance and great application value in federated learning (FL). However,current studies do not provide sufficient evidence to show which one performsbetter. Although from the optimization perspective, decentralized methods canapproach the comparable convergence of centralized methods with lesscommunication, its test performance has always been inefficient in empiricalstudies. To comprehensively explore their behaviors in FL, we study theirexcess risks, including the joint analysis of both optimization andgeneralization. We prove that on smooth non-convex objectives, 1) centralizedFL (CFL) always generalizes better than decentralized FL (DFL); 2) fromperspectives of the excess risk and test error in CFL, adopting partialparticipation is superior to full participation; and, 3) there is a necessaryrequirement for the topology in DFL to avoid performance collapse as thetraining scale increases. Based on some simple hardware metrics, we couldevaluate which framework is better in practice. Extensive experiments areconducted on common setups in FL to validate that our theoretical analysis iscontextually valid in practical scenarios.

Which mode is better for federated learning? Centralized or Decentralized