Edge-Assisted Hierarchical Federated Learning with Non-IID Data
Year: 2019
Type: preprint
Abstract: Federated Learning (FL) is capable of leveraging massively distributedprivate data, e.g., on mobile phones and IoT devices, to collaboratively train
a shared machine learning model with the help of a cloud server. However, its
iterative training process results in intolerable communication latency, and
causes huge burdens on the backbone network. Thus, reducing the communication
overhead is critical to implement FL in practice. Meanwhile, the model
performance degradation due to the unique non-IID data distribution at
different devices is another big issue for FL. In this paper, by introducing
the mobile edge computing platform as an intermediary structure, we propose a
hierarchical FL architecture to reduce the communication rounds between users
and the cloud. In particular, a Hierarchical Federated Averaging (HierFAVG)
algorithm is proposed, which allows multiple local aggregations at each edge
server before one global aggregation at the cloud. We establish the convergence
of HierFAVG for both convex and non-convex objective functions with non-IID
user data. It is demonstrated that HierFAVG can reach a desired model
performance with less communication, and outperform the traditional Federated
Averaging algorithm.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2945996667