FL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank, Task-Specific Adapter Clustering

no code implementations • 23 Apr 2024 • Siqi Ping, Yuzhu Mao, Yang Liu, Xiao-Ping Zhang, Wenbo Ding

Although large-scale pre-trained models hold great potential for adapting to downstream tasks through fine-tuning, the performance of such fine-tuned models is often limited by the difficulty of collecting sufficient high-quality, task-specific data.

Clustering Federated Learning

AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy

no code implementations • 1 Aug 2023 • Zihao Zhao, Yuzhu Mao, Zhenpeng Shi, Yang Liu, Tian Lan, Wenbo Ding, Xiao-Ping Zhang

In response, this paper introduces AQUILA (adaptive quantization in device selection strategy), a novel adaptive framework devised to effectively handle these issues, enhancing the efficiency and robustness of FL.

Federated Learning Privacy Preserving +1

SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications

no code implementations • 5 Apr 2022 • Yuzhu Mao, Zihao Zhao, Meilin Yang, Le Liang, Yang Liu, Wenbo Ding, Tian Lan, Xiao-Ping Zhang

It is demonstrated that SAFARI under unreliable communications is guaranteed to converge at the same rate as the standard FedAvg with perfect communications.

Federated Learning Sparse Learning