most citedAHSecAgg and TSKG: Lightweight Secure Aggregation for Federated Learning Without Compromise

2 citations · 6 across the 11 of their papers we have counts for

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cs.LG2023

Knowledge Rumination for Client Utility Evaluation in Heterogeneous Federated Learning

Xiaorui Jiang, Yu Gao, Hengwei Xu +3

Federated Learning (FL) allows several clients to cooperatively train machine learning models without disclosing the raw data. In practical applications, asynchronous FL (AFL) can…