Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources

TL;DR

Flotilla is a scalable, modular federated learning framework designed for heterogeneous edge resources, supporting asynchronous aggregation, fault tolerance, and real-world deployment, outperforming existing frameworks in resource efficiency and scalability.

Contribution

Introduces Flotilla, a flexible federated learning framework supporting asynchronous strategies, fault tolerance, and real-world deployment on diverse hardware, with a modular and resource-efficient design.

Findings

Supports multiple FL strategies with modular design

Demonstrates fault tolerance with rapid failover

Achieves comparable or better resource usage and scalability than existing frameworks

Abstract

With the recent improvements in mobile and edge computing and rising concerns of data privacy, Federated Learning(FL) has rapidly gained popularity as a privacy-preserving, distributed machine learning methodology. Several FL frameworks have been built for testing novel FL strategies. However, most focus on validating the learning aspects of FL through pseudo-distributed simulation but not for deploying on real edge hardware in a distributed manner to meaningfully evaluate the federated aspects from a systems perspective. Current frameworks are also inherently not designed to support asynchronous aggregation, which is gaining popularity, and have limited resilience to client and server failures. We introduce Flotilla, a scalable and lightweight FL framework. It adopts a ``user-first'' modular design to help rapidly compose various synchronous and asynchronous FL strategies while being agnostic to the DNN architecture. It uses stateless clients and a server design that separates out the session state, which are periodically or incrementally checkpointed. We demonstrate the modularity of Flotilla by evaluating five different FL strategies for training five DNN models. We also evaluate the client and server-side fault tolerance on 200+ clients, and showcase its ability to rapidly failover within seconds. Finally, we show that Flotilla's resource usage on Raspberry Pis and Nvidia Jetson edge accelerators are comparable to or better than three state-of-the-art FL frameworks, Flower, OpenFL and FedML. It also scales significantly better compared to Flower for 1000+ clients. This positions Flotilla as a competitive candidate to build novel FL strategies on, compare them uniformly, rapidly deploy them, and perform systems research and optimizations.