# Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networks

arXiv: https://arxiv.org/abs/2604.10662 · HTML: https://mlsanity.com/paper/2604.10662/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 51 references that are in the map.
- First posted on arXiv 12 Apr 2026.

## Builds on (most cited first, 23 of 23)

- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Optimization Methods for Large-Scale Machine Learning](https://mlsanity.com/paper/1606.04838/) (2016)
- [Convex Optimization: Algorithms and Complexity](https://mlsanity.com/paper/1405.4980/) (2014)
- [Lipschitz regularity of deep neural networks: analysis and efficient estimation](https://mlsanity.com/paper/1805.10965/) (2018)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [Fast-Convergent Federated Learning](https://mlsanity.com/paper/2007.13137/) (2020)
- [Task-Oriented Multi-User Semantic Communications for VQA Task](https://mlsanity.com/paper/2108.07357/) (2021)
- [Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraints](https://mlsanity.com/paper/2106.00490/) (2021)
- [Goal-Driven Autonomous Exploration Through Deep Reinforcement Learning](https://mlsanity.com/paper/2103.07119/) (2021)
- [Accelerating DNN Training in Wireless Federated Edge Learning Systems](https://mlsanity.com/paper/1905.09712/) (2019)
- [Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey](https://mlsanity.com/paper/2110.13484/) (2021)
- [Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI](https://mlsanity.com/paper/2207.00969/) (2022)
- [Coded Computing for Low-Latency Federated Learning over Wireless Edge Networks](https://mlsanity.com/paper/2011.06223/) (2020)
- [Machine Intelligence at the Edge with Learning Centric Power Allocation](https://mlsanity.com/paper/1911.04922/) (2019)
- [RDA: An Accelerated Collision Free Motion Planner for Autonomous Navigation in Cluttered Environments](https://mlsanity.com/paper/2210.00192/) (2022)
- [Toward Ambient Intelligence: Federated Edge Learning with Task-Oriented Sensing, Computation, and Communication Integration](https://mlsanity.com/paper/2206.05949/) (2022)
- [Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving](https://mlsanity.com/paper/2306.16169/) (2023)
- [EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehicles](https://mlsanity.com/paper/2101.06056/) (2021)
- [Edge Learning for Large-Scale Internet of Things With Task-Oriented Efficient Communication](https://mlsanity.com/paper/2305.00383/) (2023)
- [UGV-assisted Wireless Powered Backscatter Communications for Large-Scale IoT Networks](https://mlsanity.com/paper/2110.02513/) (2021)
- [Large-Scale Bandwidth and Power Optimization for Multi-Modal Edge Intelligence Autonomous Driving](https://mlsanity.com/paper/2210.09659/) (2022)
- [Robotic Sensor Network: Achieving Mutual Communication Control Assistance With Fast Cross-Layer Optimization](https://mlsanity.com/paper/2404.10541/) (2024)
- [System-Level Simulation Framework for NB-IoT: Key Features and Performance Evaluation](https://mlsanity.com/paper/2407.17691/) (2024)

## Abstract (arXiv)

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.