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AFLGuard: Byzantine-robust Asynchronous Federated Learning
Asynchronous FL aims to address this challenge by enabling the server to update the model once any client's model update reaches it without waiting for other clients' model updates.
CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning
This matches the convergence rate of distributed/federated learning without compression, thus achieving high communication efficiency while not sacrificing learning accuracy in FL.
GT-STORM: Taming Sample, Communication, and Memory Complexities in Decentralized Non-Convex Learning
Decentralized nonconvex optimization has received increasing attention in recent years in machine learning due to its advantages in system robustness, data privacy, and implementation simplicity.
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