SPIDER: Searching Personalized Neural Architecture for Federated Learning

Understand

Federated learning (FL) is an efficient learning framework that assists distributed machine learning when data cannot be shared with a centralized server due to privacy and regulatory restrictions.

• Recent advancements in FL use predefined architecture-based learning for all the clients.
• However, given that clients' data are invisible to the server and data distributions are non-identical across clients, a predefined architecture discovered in a centralized setting may not be an optimal solution for all the clients in FL.
• Motivated by this challenge, in this work, we introduce SPIDER, an algorithmic framework that aims to Search Personalized neural architecture for federated learning.