Protea: Client Profiling within Federated Systems using Flower

07/03/2022

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Federated Learning (FL) has emerged as a prospective solution that facilitates the training of a high-performing centralised model without compromising the privacy of users. While successful, research is currently limited by the possibility of establishing a realistic large-scale FL system at the early stages of experimentation. Simulation can help accelerate this process. To facilitate efficient scalable FL simulation of heterogeneous clients, we design and implement Protea, a flexible and lightweight client profiling component within federated systems using the FL framework Flower. It allows automatically collecting system-level statistics and estimating the resources needed for each client, thus running the simulation in a resource-aware fashion. The results show that our design successfully increases parallelism for 1.66 × faster wall-clock time and 2.6× better GPU utilisation, which enables large-scale experiments on heterogeneous clients.

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Protea: Client Profiling within Federated Systems using Flower

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Protea: Client Profiling within Federated Systems using Flower

Related Research

High-throughput Simulation of Federated Learning via Resource-Aware Client Placement

Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning

FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training

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