Enabling Fine-Tuning of Direct Feedback Alignment via Feedback-Weight Matching

Although Direct Feedback Alignment (DFA) has demonstrated potential by enabling efficient and parallel updates of weight parameters through direct propagation of the network's output error, its usage has been primarily restricted to training networks from scratch. In this paper, we introduce feedback-weight matching, a first method that enables reliable fine-tuning of fully connected neural networks using DFA. We provide an analysis showing that existing standard DFA struggles to fine-tune networks that are pre-trained via back-propagation. Through a thorough analysis of weight alignment (WA) and gradient alignment (GA), we demonstrate that the proposed feedback-weight matching enhances DFA's ability and stability in fine-tuning, which provides useful insights into DFA's behavior and characteristics when applied to fine-tuning. In addition, we prove that feedback-weight matching, when combined with weight decay, not only mitigates over-fitting but also further reduces the network output error, leading to improved learning performance during DFA-based fine-tuning. Experimental results show that feedback-weight matching, for the first time, enables reliable fine-tuning across various fine-tuning tasks, compared to existing standard DFA, e.g., achieving 7.97% accuracy improvement on image classification tasks (82.67% vs. 74.70%) and 0.66 higher correlation score on NLP tasks (0.76 vs. 0.10). The code is available on an anonymous GitHub.