Papers
1–3 of 3Research Paper·Feb 20, 2026
FedZMG: Efficient Client-Side Optimization in Federated Learning
Federated Learning (FL) enables distributed model training on edge devices while preserving data privacy. However, clients tend to have non-Independent and Identically Distributed (non-IID) data, whic...
7.0 viability
Research Paper·Feb 27, 2026
FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA
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 averaging used to p...
4.0 viability
Research Paper·Feb 12, 2026
Gradient Compression May Hurt Generalization: A Remedy by Synthetic Data Guided Sharpness Aware Minimization
It is commonly believed that gradient compression in federated learning (FL) enjoys significant improvement in communication efficiency with negligible performance degradation. In this paper, we find ...
1.0 viability