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cs.LG2025
PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning
Liangyan Li, Yangyi Liu, Yimo Ning +2
Federated Learning (FL) has emerged as a powerful paradigm for leveraging diverse datasets from multiple sources while preserving data privacy by avoiding centralized storage. Howe…
cs.LG2025
PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning
Ori Peleg, Natalie Lang, Dan Ben Ami +3
Federated learning (FL) enables multiple edge devices to collaboratively train a machine learning model without the need to share potentially private data. Federated learning proce…
cs.LG2024
Age-of-Gradient Updates for Federated Learning over Random Access Channels
Yu Heng Wu, Houman Asgari, Stefano Rini +1
This paper studies the problem of federated training of a deep neural network (DNN) over a random access channel (RACH) such as in computer networks, wireless networks, and cellula…