Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning

• URL: http://arxiv.org/abs/2106.04911v4
• Date: Mon, 24 Apr 2023 20:21:31 GMT
• Title: Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning
• Authors: Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang
• Abstract summary: Model-agnostic meta-learning (MAML) has become a popular research area. Existing MAML algorithms rely on the episode' idea by sampling a few tasks and data points to update the meta-model at each iteration. This paper proposes memory-based algorithms for MAML that converge with vanishing error.
• Score: 56.17603785248675
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, model-agnostic meta-learning (MAML) has become a popular research area. However, the stochastic optimization of MAML is still underdeveloped. Existing MAML algorithms rely on the ``episode'' idea by sampling a few tasks and data points to update the meta-model at each iteration. Nonetheless, these algorithms either fail to guarantee convergence with a constant mini-batch size or require processing a large number of tasks at every iteration, which is unsuitable for continual learning or cross-device federated learning where only a small number of tasks are available per iteration or per round. To address these issues, this paper proposes memory-based stochastic algorithms for MAML that converge with vanishing error. The proposed algorithms require sampling a constant number of tasks and data samples per iteration, making them suitable for the continual learning scenario. Moreover, we introduce a communication-efficient memory-based MAML algorithm for personalized federated learning in cross-device (with client sampling) and cross-silo (without client sampling) settings. Our theoretical analysis improves the optimization theory for MAML, and our empirical results corroborate our theoretical findings. Interested readers can access our code at \url{https://github.com/bokun-wang/moml}.

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