Split Federated Learning Architectures for High-Accuracy and Low-Delay Model Training

TL;DR

This paper introduces a novel architecture for Split Federated Learning that optimizes model accuracy, reduces training delay, and decreases communication overhead by jointly considering partitioning layers and client-to-aggregator assignments.

Contribution

It formulates a joint optimization problem for HSFL architectures, proves NP-hardness, and proposes the first heuristic algorithm that balances accuracy with delay efficiency.

Findings

Improves accuracy by 3% over existing methods.

Reduces training delay by 20%.

Cuts communication overhead by 50%.

Abstract

Can we find a network architecture for ML model training so as to optimize training loss (and thus, accuracy) in Split Federated Learning (SFL)? And can this architecture also reduce training delay and communication overhead? While accuracy is not influenced by how we split the model in ordinary, state-of-the-art SFL, in this work we answer the questions above in the affirmative. Recent Hierarchical SFL (HSFL) architectures adopt a three-tier training structure consisting of clients, (local) aggregators, and a central server. In this architecture, the model is partitioned at two partitioning layers into three sub-models, which are executed across the three tiers. Despite their merits, HSFL architectures overlook the impact of the partitioning layers and client-to-aggregator assignments on accuracy, delay, and overhead. This work explicitly captures the impact of the partitioning layers and client-to-aggregator assignments on accuracy, delay and overhead by formulating a joint optimization problem. We prove that the problem is NP-hard and propose the first accuracy-aware heuristic algorithm that explicitly accounts for model accuracy, while remaining delay-efficient. Simulation results on public datasets show that our approach can improve accuracy by 3%, while reducing delay by 20% and overhead by 50%, compared to state-of-the-art SFL and HSFL schemes.