1 citations · 1 across the 4 of their papers we have counts for
4 papers
FedSteer: Taming Extreme Gradient Staleness in Federated Learning with Corrective Projections and Caching
Haoran Zhang, Cainã Figueiredo Pereira, Marie Siew +3
Federated learning (FL) is often subject to aggregation variance if clients do not consistently participate in training rounds. While reusing stale model updates from inactive clie…
FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA
Haoran Zhang, Dongjun Kim, Seohyeon Cha +1
Federated LoRA provides a communication-efficient mechanism for fine-tuning large language models on decentralized data. In practice, however, a discrepancy between the factor-wise…
Towards Optimal Heterogeneous Client Sampling in Multi-Model Federated Learning
Haoran Zhang, Zejun Gong, Zekai Li +3
Federated learning (FL) allows edge devices to collaboratively train models without sharing local data. As FL gains popularity, clients may need to train multiple unrelated FL mode…
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…