Adaptive Federated Learning in Resource Constrained Edge Computing Systems
This addresses resource-efficient federated learning for edge computing systems, offering an incremental improvement by optimizing local-global trade-offs under constraints.
The paper tackles the problem of training machine learning models on data distributed across edge nodes without centralizing raw data, proposing a control algorithm that optimizes the trade-off between local updates and global aggregation to minimize loss under resource constraints, achieving near-optimal performance in experiments with real datasets.
Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradient-descent based approaches. We analyze the convergence bound of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best trade-off between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.