no code implementations • 7 Aug 2023 • Lumin Liu, Jun Zhang, Shenghui Song, Khaled B. Letaief
To improve communication efficiency and achieve a better privacy-utility trade-off, we propose a communication-efficient FL training algorithm with differential privacy guarantee.
no code implementations • 20 Jul 2023 • Jiawei Shao, Zijian Li, Wenqiang Sun, Tailin Zhou, Yuchang Sun, Lumin Liu, Zehong Lin, Yuyi Mao, Jun Zhang
Without data centralization, FL allows clients to share local information in a privacy-preserving manner.
no code implementations • 14 Mar 2022 • Lumin Liu, Jun Zhang, S. H. Song, Khaled B. Letaief
Federated Distillation (FD) is a recently proposed alternative to enable communication-efficient and robust FL, which achieves orders of magnitude reduction of the communication overhead compared with FedAvg and is flexible to handle heterogeneous models at the clients.
no code implementations • 26 Mar 2021 • Lumin Liu, Jun Zhang, Shenghui Song, Khaled B. Letaief
Hierarchical FL, with a client-edge-cloud aggregation hierarchy, can effectively leverage both the cloud server's access to many clients' data and the edge servers' closeness to the clients to achieve a high communication efficiency.
1 code implementation • 16 May 2019 • Lumin Liu, Jun Zhang, S. H. Song, Khaled B. Letaief
To combine their advantages, we propose a client-edge-cloud hierarchical Federated Learning system, supported with a HierFAVG algorithm that allows multiple edge servers to perform partial model aggregation.