<ul><li>Federated fine-tuning is a promising approach for adapting foundation models to downstream tasks using decentralized data.</li><li>Challenges in real-world deployment include high computational demands and communication requirements, especially when dealing with heterogeneous and constrained data and resources on client devices.</li><li>A new framework called AFLoRA has been proposed to address these challenges, which separates shared and client-specific updates, incorporates rank pruning, and utilizes rank-aware aggregation for better performance.</li><li>Extensive experiments show that AFLoRA outperforms existing methods in accuracy and efficiency, providing a practical solution for adapting Large Language Models in heterogeneous environments.</li></ul>

AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption

Discover more