Robust Federated Fine-Tuning with Adaptive Aggregation

This paper addresses the practical challenges of Federated Fine-Tuning (FFT) in real-world scenarios, specifically focusing on unreliable connections and heterogeneous data distributions. The proposed FedAuto framework offers a plug-and-play solution that doesn't require prior knowledge of network conditions, making it highly adaptable. The rigorous convergence guarantee, which removes common assumptions about connection failures, is a significant contribution. The experimental results further validate the effectiveness of FedAuto.

• •Proposes FedAuto, a novel FFT framework for heterogeneous networks with unreliable connections.
• •FedAuto uses adaptive aggregation to handle connection failures and data heterogeneity.
• •Provides a rigorous convergence guarantee without assumptions on connection failure probabilities.
• •Demonstrates superior performance compared to state-of-the-art baselines in diverse scenarios.