Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning

TL;DR

Flight is a new federated learning framework that supports complex multi-tier topologies, asynchronous aggregation, and significantly improves scalability and communication efficiency over existing solutions like Flower.

Contribution

It introduces a hierarchical, FaaS-based FL framework that decouples control and data planes, enabling better scalability and reduced communication overheads.

Findings

Supports up to 2048 devices, scaling beyond existing frameworks.

Reduces FL makespan across multiple models.

Decreases communication overheads by over 60%.

Abstract

Federated Learning (FL) is a decentralized machine learning paradigm where models are trained on distributed devices and are aggregated at a central server. Existing FL frameworks assume simple two-tier network topologies where end devices are directly connected to the aggregation server. While this is a practical mental model, it does not exploit the inherent topology of real-world distributed systems like the Internet-of-Things. We present Flight, a novel FL framework that supports complex hierarchical multi-tier topologies, asynchronous aggregation, and decouples the control plane from the data plane. We compare the performance of Flight against Flower, a state-of-the-art FL framework. Our results show that Flight scales beyond Flower, supporting up to 2048 simultaneous devices, and reduces FL makespan across several models. Finally, we show that Flight's hierarchical FL model can reduce communication overheads by more than 60%.