Large-scale Internet of Things (IoT) networks enable intelligent services such as smart cities and autonomous driving, but often face resource constraints. Collecting heterogeneous sensory data, especially in small-scale datasets, is challenging, and independent edge nodes can lead to inefficient resource utilization and reduced learning performance. To address these issues, this paper proposes a collaborative optimization framework for energy-efficient federated edge learning with small-scale datasets. We first derive an expected learning loss to quantify the relationship between the number of training samples and learning objectives. A stochastic online learning algorithm is then designed to adapt to data variations, and a resource optimization problem with a convergence bound is formulated. Finally, an online distributed algorithm efficiently solves large-scale optimization problems with high scalability. Extensive simulations and autonomous navigation case studies with collision avoidance demonstrate that the proposed approach significantly improves learning performance and resource efficiency compared to state-of-the-art benchmarks.

Federated learning (FL) enables wireless terminals to collaboratively learn a shared parameter model while keeping all the training data on devices per se. Parameter sharing consists of synchronous and asynchronous ways: the former transmits parameters as blocks or frames and waits until all transmissions finish, whereas the latter provides messages about the status of pending and failed parameter transmission requests. Whatever synchronous or asynchronous parameter sharing is applied, the learning model shall adapt to distinct network architectures as an improper learning model will deteriorate learning performance and, even worse, lead to model divergence for the asynchronous transmission in resource-limited large-scale Internet-of-Things (IoT) networks. This paper proposes a decentralized learning model and develops an asynchronous parameter-sharing algorithm for resource-limited distributed IoT networks. This decentralized learning model approaches a convex function as the number of nodes increases, and its learning process converges to a global stationary point with a higher probability than the centralized FL model. Moreover, by jointly accounting for the convergence bound of federated learning and the transmission delay of wireless communications, we develop a node scheduling and bandwidth allocation algorithm to minimize the transmission delay. Extensive simulation results corroborate the effectiveness of the distributed algorithm in terms of fast learning model convergence and low transmission delay.

In Internet of Things (IoT) networks, edge learning for data-driven tasks provides intelligent applications and services. As the network size becomes large, different users may generate distinct datasets. Thus, to suit multiple edge learning tasks for large-scale IoT networks, this paper considers efficient communication under a task-oriented principle by using the collaborative design of wireless resource allocation and edge learning error prediction. In particular, we start with multi-user scheduling to alleviate co-channel interference in dense networks. Then, we perform optimal power allocation in parallel for different learning tasks. Thanks to the high parallelization of the designed algorithm, extensive experimental results corroborate that the multi-user scheduling and task-oriented power allocation improve the performance of distinct edge learning tasks efficiently compared with the state-of-the-art benchmark algorithms.