SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning

Recent years have witnessed significant progress in a variety of emerging areas including meta-learning and fine-tuning [11, 52], automated hyperparameter optimization [13, 10], reinforcement learning [31, 21], fair batch selection in machine learning [54], adversarial learning [76, 40], AI-aware communication networks [27], fairness-aware federated learning [75], etc. These problems share a common nested optimization structure, and have inspired intensive study on the theory and algorithmic development of bilevel optimization. Prior efforts have been taken mainly on the single-machine scenario. However, in modern machine learning applications, data privacy has emerged as a critical concern in centralized training, and the data often exhibit an inherently distributed nature [70]. This highlights the importance of recent research and attention on federated bilevel optimization, and has inspired many emerging applications including but not limited to federated meta-learning [9], hyperparameter tuning for federated learning [25], resource allocation over communication networks [27] and graph-aided federated learning [71], adversarial robustness on edge computing [46], etc.