Social-Aware Distributed Meta-Learning: A Perspective of Constrained Graphical Bandits

Meta-learning has earned its wide popularity to handle a family of similar tasks (e.g., classification of pets and wildlife) with elaborately trained meta-knowledge (e.g., shared network architecture and neural network parameter initialization). In this paper, we focus on the distributed training of meta-knowledge via server-device collaboration at the edge (i.e., distributed meta-learning). Notably, its practical implementation often runs into concerns like 1) time-varying unknown wireless dynamics (e.g., transmission latency); 2) device-side fair device involvement in distributed training; 3) server-side resource efficiency. To address such concerns, 1) we employ online learning to estimate the unknown dynamics and further exploit social ties among device users to accelerate online learning; 2) we utilize online control techniques to handle long-term fairness and resource constraints. By characterizing inter-user social ties as a social graph, we study distributed meta-learning from the perspective of constrained graphical bandits. Therefore, we propose a SoCial-awarE meta-kNowledge dispaTch (SCENT) algorithm by effectively integrating graphical bandit learning and online control. Besides a sublinear regret (i.e., loss of performance), SCENT also guarantees a well-trained meta-knowledge under within-budget resource consumption and fair device involvement. We conduct simulations to justify the outperformance of SCENT compared with baselines.