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cs.LG2023
Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradient
Rongfei Fan, Xuming An, Shiyuan Zuo +1
Over-the-air computation is a communication-efficient solution for federated learning (FL). In such a system, iterative procedure is performed: Local gradient of private loss funct…
cs.LG2023
Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learning
Xuming An, Rongfei Fan, Shiyuan Zuo +3
Federated learning (FL) has emerged as an appealing machine learning approach to deal with massive raw data generated at multiple mobile devices, {which needs to aggregate the trai…
cs.LG2023
FedABC: Targeting Fair Competition in Personalized Federated Learning
Dui Wang, Li Shen, Yong Luo +4
Federated learning aims to collaboratively train models without accessing their client's local private data. The data may be Non-IID for different clients and thus resulting in poo…