Model CBOR Serialization for Federated Learning

TL;DR

This paper introduces a CBOR-based message framework optimized for resource-constrained devices in federated learning, significantly reducing communication message sizes compared to JSON.

Contribution

It presents a generic CBOR serialization framework tailored for federated learning, improving communication efficiency on low-power, lossy networks.

Findings

Messages are up to 75% smaller than JSON-based messages.

Framework is compatible with existing federated learning systems.

Evaluations include real-world model benchmarks.

Abstract

The typical federated learning workflow requires communication between a central server and a large set of clients synchronizing model parameters between each other. The current frameworks use communication protocols not suitable for resource-constrained devices and are either hard to deploy or require high-throughput links not available on these devices. In this paper, we present a generic message framework using CBOR for communication with existing federated learning frameworks optimised for use with resource-constrained devices and low power and lossy network links. We evaluate the resulting message sizes against JSON serialized messages where compare both with model parameters resulting in optimal and worst case serialization length, and with a real-world LeNet-5 model. Our benchmarks show that with our approach, messages are up to 75 % smaller in size when compared to the JSON alternative.