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In order to meet the requirements for safety and latency in many IoT applications, intelligent decisions must be made right here right now at the network edge, calling for edge intelligence. To facilitate fast edge learning, this work advocates a platform-aided federated meta-learning architecture, where a set of edge nodes joint force to learn a meta-model (i.e., model initialization for adaptation in a new learning task) by exploiting the similarity among edge nodes as well as the cloud knowledge transfer. The federated meta-learning problem is cast as a regularized stochastic optimization problem, using Bregman Divergence between the edge model and the cloud pre-trained model as the regularization. We then devise an alternating direction method of multiplier (ADMM) based Hessian-free federated meta-learning algorithm, called ADMM-FedMeta, with inexact Hessian estimation. Further, we analyze the convergence properties and the rapid adaptation performance of ADMM-FedMeta for the general non-convex case. The theoretical results show that under mild conditions, ADMM-FedMeta converges to an $$\epsilon$$-approximate first-order stationary point after at most $$\mathcal{O}(1/\epsilon^2)$$ communication rounds. Extensive experimental studies on benchmark datasets demonstrate the effectiveness and efficiency of ADMM-FedMeta, and showcase that ADMM-FedMeta outperforms the existing baselines.
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Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates
Federated reinforcement learning (FedRL) enables agents to collaboratively train a global policy without sharing their individual data. However, high communication overhead remains a critical bottleneck, particularly for natural policy gradient (NPG) methods, which are second-order. To address this issue, we propose the FedNPG-ADMM framework, which leverages the alternating direction method of multipliers (ADMM) to approximate global NPG directions efficiently. We theoretically demonstrate that using ADMM-based gradient updates reduces communication complexity from $O(d^2)$ to $O(d)$ at each iteration, where $$d$$ is the number of model parameters. Furthermore, we show that achieving an $$\epsilon$$-error stationary convergence requires $$O(\frac{1}{(1-\gamma)^2-\epsilon})$$ iterations for discount factor $$\gamma$$, demonstrating that FedNPG-ADMM maintains the same convergence rate as standard FedNPG. Through evaluation of the proposed algorithms in MuJoCo environments, we demonstrate that FedNPG-ADMM maintains the reward performance of standard FedNPG, and that its convergence rate improves when the number of federated agents increases.
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- Award ID(s):
- 2231350
- PAR ID:
- 10543043
- Editor(s):
- Oh, A; Naumann, T; Globerson, A; Saenko, K; Hardt, M; Levine, S
- Publisher / Repository:
- Neural Information Processing Systems
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
