3 papers
cs.CR2025
λ-SecAgg: Partial Vector Freezing for Lightweight Secure Aggregation in Federated Learning
Siqing Zhang, Yong Liao, Pengyuan Zhou
Secure aggregation of user update vectors (e.g. gradients) has become a critical issue in the field of federated learning. Many Secure Aggregation Protocols (SAPs) face exorbitant…
cs.LG2025
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…
cs.LG2024
Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendation
Siqing Zhang, Yuchen Ding, Wei Tang +3
Under stringent privacy constraints, whether federated recommendation systems can achieve group fairness remains an inadequately explored question. Taking gender fairness as a repr…