Federated Neural Architecture Search for Efficient and Privacy-Preserving Model Training
Tamai Ramírez-Gordillo, Higinio Mora, Francisco A. Pujol, Antonio Maciá-Lillo · 2024
This article introduces Fed-NAS, a novel framework that combines Federated Learning (FL) with Efficient Neural Architecture Search (ENAS) to allow for distributed neural architecture optimization while maintaining privacy. In Fed-NAS, various clients run the ENAS algorithm on their own data and then send the results to a central server, which combines them into a global weight dictionary. This dictionary helps clients transfer weight efficiently, encouraging sharing of knowledge and speeding up training. The framework's effectiveness is illustrated through its performance on the CIFAR-10 dataset, surpassing that of traditional ENAS methods. Furthermore, the future inclusion of blockchain technology will improve the confidentiality and decentralization aspects of the system. Upcoming research will also be concentrated on expanding Fed-NAS to operate on multiple GPUs and devices to assess its performance and scalability, while also comparing it to traditional fixed-architecture Federated Learning systems to confirm the advantages of dynamic architecture adjustment and parameter sharing.