Client Orchestration and Cost-Efficient Joint Optimization for
  NOMA-Enabled Hierarchical Federated Learning
Bibo Wu Fang Fang Xianbin Wang Donghong Cai Shu Fu Zhiguo Ding
Hierarchical federated learning (HFL) shows great advantages over
conventional two-layer federated learning (FL) in reducing network overhead and
interaction latency while still retaining the data privacy of distributed FL
clients. However, the communication and energy overhead still pose a bottleneck
for HFL performance, especially as the number of clients raises dramatically.
To tackle this issue, we propose a non-orthogonal multiple access (NOMA)
enabled HFL system under semi-synchronous cloud model aggregation in this
paper, aiming to minimize the total cost of time and energy at each HFL global
round. Specifically, we first propose a novel fuzzy logic based client
orchestration policy considering client heterogenerity in multiple aspects,
including channel quality, data quantity and model staleness. Subsequently,
given the fuzzy based client-edge association, a joint edge server scheduling
and resource allocation problem is formulated. Utilizing problem decomposition,
we firstly derive the closed-form solution for the edge server scheduling
subproblem via the penalty dual decomposition (PDD) method. Next, a deep
deterministic policy gradient (DDPG) based algorithm is proposed to tackle the
resource allocation subproblem considering time-varying environments. Finally,
extensive simulations demonstrate that the proposed scheme outperforms the
considered benchmarks regarding HFL performance improvement and total cost
reduction.