Federated Multi-Level Optimization over Decentralized Networks
This addresses the problem of scalable distributed optimization for large-scale systems where centralized methods are impractical, though it appears incremental as it builds on existing multi-level optimization frameworks.
The paper tackles distributed multi-level optimization over decentralized networks by proposing a gossip-based algorithm that enables networked agents to solve optimization problems at different levels in a single timescale, achieving optimal sample complexity that scales linearly with network size and demonstrating state-of-the-art performance in applications like hyper-parameter tuning and decentralized reinforcement learning.
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.