Apr 22, 2022 · An optimized participation-aware federated learning algorithm called AdaFed, which can adaptively tune the aggregation weight of each device.
We further propose an optimized participation-aware federated learning algorithm called AdaFed, which can adaptively tune the aggregation weight of each device ...
In this section, we will introduce a participation-aware federated learning algorithm called AdaFed to handle the problem of biased device participation. The ...
We further propose an optimized participation-aware federated learning algorithm called AdaFed , which can adaptively tune the aggregation weight of each device ...
AdaFed: Optimizing Participation-Aware Federated Learning With Adaptive Aggregation Weights. L Tan, X Zhang, Y Zhou, X Che, M Hu, X Chen, D Wu. IEEE ...
Our analysis reveals that biased client participation can significantly reduce the precision of the FL model. We validate this through trace-driven experiments, ...
Jul 25, 2024 · Adafed: Optimizing participation-aware fed- erated learning with adaptive aggregation weights. IEEE. Transactions on Network Science and ...
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Aug 23, 2023 · Adaptive aggregation is a method for reducing communication costs in FL systems. In FL, data are typically distributed across multiple devices, ...
Jan 10, 2024 · The goal of AdaFed is to find an updating direction for the server along which (i) all the clients' loss functions are decreasing; and (ii) more importantly, ...
In this study, we propose an Uncertainty-Aware Federated Reinforcement Learning (UA-FedRL) method that dynamically selects epochs of individual clients.