Adaptive Federated Learning in Resource Constrained Edge Computing Systems

TLDR

Emerging IoT, social networking, and crowd‑sourcing applications generate large amounts of edge data, yet bandwidth, storage, and privacy constraints make centralized model training impractical. This work addresses learning model parameters from data distributed across edge nodes without transmitting raw data to a central server. We analyze convergence bounds for distributed gradient‑descent models and propose a resource‑aware control algorithm that optimally balances local updates and global aggregation, then evaluate it on real datasets in prototype and simulated systems. Experiments demonstrate that the algorithm achieves near‑optimal performance across various machine learning models and data distributions.

Abstract

Emerging technologies and applications including Internet of Things, 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 tradeoff 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.