dsnmod
Abstract
Federated learning (FL) is becoming a popular paradigm
for collaborative learning over distributed, private datasets
owned by non-trusting entities. FL has seen successful deployment in production environments, and it has been adopted
in services such as virtual keyboards, auto-completion, item
recommendation, and several IoT applications. However, FL
comes with the challenge of performing training over largely
heterogeneous datasets, devices, and networks that are out
of the control of the centralized FL server. Motivated by this
inherent setting, we make a first step towards characterizing the impact of device and behavioral heterogeneity on
the trained model. We conduct an extensive empirical study
spanning close to 1.5K unique configurations on five popular
FL benchmarks. Our analysis shows that these sources of
heterogeneity have a major impact on both model performance and fairness, thus shedding light on the importance
of considering heterogeneity in FL system design.
