Problem
Research questions and friction points this paper is trying to address.
Federated Learning
customizability
performance
Efficiently Distributed Federated Learning
🤖 AI Summary
研究通过开发高效、可定制的开源联邦学习框架FastFederatedLearning(FFL)，解决了现有框架在性能和灵活性上的不足，实现了跨平台加速。
📝 Abstract
Federated Learning (FL) is experiencing a substantial research interest, with many frameworks being developed to allow practitioners to build federations easily and quickly. Most of these efforts do not consider two main aspects that are key to Machine Learning (ML) software: customizability and performance. This research addresses these issues by implementing an open-source FL framework named FastFederatedLearning (FFL). FFL is implemented in C/C++, focusing on code performance, and allows the user to specify any communication graph between clients and servers involved in the federation, ensuring customizability. FFL is tested against Intel OpenFL, achieving consistent speedups over different computational platforms (x86-64, ARM-v8, RISC-V), ranging from 2.5x and 3.69x. We aim to wrap FFL with a Python interface to ease its use and implement a middleware for different communication backends to be used. We aim to build dynamic federations in which relations between clients and servers are not static, giving life to an environment where federations can be seen as long-time evolving structures and exploited as services.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Efficiently Distributed Federated Learning
customizability
performance
communication graph
dynamic federations
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