Monday, May 20 • 1:50pm - 2:10pm
Towards Taming the Resource and Data Heterogeneity in Federated Learning

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Machine learning model training often require data from multiple parties. However, in some cases, data owners cannot or are not willing to share their data due to legal or privacy constraints but would still like to benefit from training a model jointly with multiple parties. To this end, federated learning (FL) has emerged as an alternative way to do collaborative model training without sharing the training data. Such collaboration leads to more accurate and performant models than any party owning a partial set of all the data sources could hope to learn in isolation.

In this paper, we study the impact of resource (e.g., CPU, memory, and network resources) and data (e.g., training dataset sizes) heterogeneity on the training time of FL. Then, we discuss the research problems and their challenges involved in taming such resource and data heterogeneity in FL systems.


Zheng Chai

George Mason University

Hannan Fayyaz

York University

Zeshan Fayyaz

Ryerson University

Ali Anwar

IBM Research–Almaden

Yi Zhou

IBM Research–Almaden

Nathalie Baracaldo

IBM Research–Almaden

Heiko Ludwig

IBM Research–Almaden

Yue Cheng

George Mason University

Monday May 20, 2019 1:50pm - 2:10pm
Stevens Creek Room

Attendees (6)