Federated Multi-Level Optimization over Decentralized Networks

• URL: http://arxiv.org/abs/2310.06217v1
• Date: Tue, 10 Oct 2023 00:21:10 GMT
• Title: Federated Multi-Level Optimization over Decentralized Networks
• Authors: Shuoguang Yang, Xuezhou Zhang, Mengdi Wang
• Abstract summary: We study the problem of distributed multi-level optimization over a network, where agents can only communicate with their immediate neighbors. We propose a novel gossip-based distributed multi-level optimization algorithm that enables networked agents to solve optimization problems at different levels in a single timescale. Our algorithm achieves optimal sample complexity, scaling linearly with the network size, and demonstrates state-of-the-art performance on various applications.
• Score: 55.776919718214224
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-level optimization has gained increasing attention in recent years, as it provides a powerful framework for solving complex optimization problems that arise in many fields, such as meta-learning, multi-player games, reinforcement learning, and nested composition optimization. In this paper, we study the problem of distributed multi-level optimization over a network, where agents can only communicate with their immediate neighbors. This setting is motivated by the need for distributed optimization in large-scale systems, where centralized optimization may not be practical or feasible. To address this problem, we propose a novel gossip-based distributed multi-level optimization algorithm that enables networked agents to solve optimization problems at different levels in a single timescale and share information through network propagation. Our algorithm achieves optimal sample complexity, scaling linearly with the network size, and demonstrates state-of-the-art performance on various applications, including hyper-parameter tuning, decentralized reinforcement learning, and risk-averse optimization.

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