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 ...
Oct 31, 2024 · Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive ...
The original FedAvg [1] algorithm averages the clients models with an a-priori weighting strategy that typically depends on the numerosity of the various ...
Jul 25, 2024 · Adafed: Optimizing participation-aware fed- erated learning with adaptive aggregation weights. IEEE. Transactions on Network Science and ...
People also ask
What is federated learning averaging techniques?
What is federated learning with additional mechanisms on clients to reduce communication costs?
Aug 23, 2023 · This paper explores the potential for communication-efficient federated learning (FL) in modern distributed systems.
In this paper, we address this problem by adapting the aggregation weights in federated averaging (FedAvg) based on the participation history of each client. We ...