Federated Machine Learning as a Self-Adaptive Problem

Luciano Baresi,Giovanni Quattrocchi,Nicholas Rasi

2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)（2021）

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摘要

Machine Learning (ML) enables the creation of a new generation of applications that “learn” from collected data, transferred and analyzed on centralized servers. Moving data may imply a significant overhead and may also undermine users' privacy. Federated Machine Learning (FedML) tries to address these issues by means of local training phases on client devices: only lightweight aggregated data are...

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关键词

federated machine learning,self adaptive systems,optimization,runtime control

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