most citedThe Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy

1 citations · 1 across the 3 of their papers we have counts for

collaborators
Showing cs.LGShow all

2 papers · 1 filter

cs.LG2024★ 1 cited

The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy

Jiating Ma, Yipeng Zhou, Qi Li +3

To preserve the data privacy, the federated learning (FL) paradigm emerges in which clients only expose model gradients rather than original data for conducting model training. To…

cs.LG2024

Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes

Xiaoxin Su, Yipeng Zhou, Laizhong Cui +2

In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model di…