
The proposed FL-TAC algorithm enables efficient fine-tuning of large pre-trained models in federated learning by training low-rank, task-specific adapters on client devices and performing clustering-based aggregation on the server to facilitate knowledge exchange across tasks.


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efficient-fine-tuning-of-large-pre-trained-models-in-federated-learning-via-low-rank-task-specific-adapter-clustering


Efficient Fine-Tuning of Large Pre-Trained Models in Federated Learning via Low-Rank, Task-Specific Adapter Clustering
