most citedEfficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability

2 citations · 3 across the 5 of their papers we have counts for

collaborators
Showing cs.LGShow all

2 papers · 1 filter

cs.LG2026

Resilience Beyond Stationary Client Unavailability: Unlocking Efficient and Unbiased Federated Learning

Ming Xiang, Stratis Ioannidis, Edmund Yeh +2

Due to resource constraints or external and internal uncertainties, clients in real-world federated learning systems are often intermittently available edge devices. In highly dyna…

cs.LG2024★ 1 cited

Fair Concurrent Training of Multiple Models in Federated Learning

Marie Siew, Haoran Zhang, Jong-Ik Park +6

Federated learning (FL) enables collaborative learning across multiple clients. In most FL work, all clients train a single learning task. However, the recent proliferation of FL a…