FedProj combines client-side gradient projection onto a global-knowledge loss with server-side ensemble distillation and outperforms existing federated learning methods on non-IID image and NLP benchmarks.
On the convergence of decentralized federated learning under imperfect information sharing
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Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data
FedProj combines client-side gradient projection onto a global-knowledge loss with server-side ensemble distillation and outperforms existing federated learning methods on non-IID image and NLP benchmarks.