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FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning
This comparative analysis revealed that due to the limited information contained within client models from other clients during the initial stages of federated learning, more substantial constraints need to be imposed on the parameters of the adaptive algorithm.
The Dog Walking Theory: Rethinking Convergence in Federated Learning
In this paper, we study the convergence of FL on non-IID data and propose a novel \emph{Dog Walking Theory} to formulate and identify the missing element in existing research.
Byzantine-Robust Federated Learning via Credibility Assessment on Non-IID Data
Federated learning is a novel framework that enables resource-constrained edge devices to jointly learn a model, which solves the problem of data protection and data islands.
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