# Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach

arXiv: https://arxiv.org/abs/2405.12046 · HTML: https://mlsanity.com/paper/2405.12046/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 54 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 29 of 29)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [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)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [Scheduling Policies for Federated Learning in Wireless Networks](https://mlsanity.com/paper/1908.06287/) (2019)
- [Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective](https://mlsanity.com/paper/2004.04314/) (2020)
- [Client Selection in Federated Learning: Principles, Challenges, and Opportunities](https://mlsanity.com/paper/2211.01549/) (2022)
- [Federated Learning: A Signal Processing Perspective](https://mlsanity.com/paper/2103.17150/) (2021)
- [Design and Analysis of Uplink and Downlink Communications for Federated Learning](https://mlsanity.com/paper/2012.04057/) (2020)
- [Energy-Efficient Radio Resource Allocation for Federated Edge Learning](https://mlsanity.com/paper/1907.06040/) (2019)
- [Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraints](https://mlsanity.com/paper/2106.00490/) (2021)
- [Federated Learning under Distributed Concept Drift](https://mlsanity.com/paper/2206.00799/) (2022)
- [Time-Varying Convex Optimization: Time-Structured Algorithms and Applications](https://mlsanity.com/paper/2006.08500/) (2020)
- [Cost-Effective Federated Learning in Mobile Edge Networks](https://mlsanity.com/paper/2109.05411/) (2021)
- [Energy-Aware Analog Aggregation for Federated Learning with Redundant Data](https://mlsanity.com/paper/1911.00188/) (2019)
- [FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction](https://mlsanity.com/paper/2006.07273/) (2020)
- [Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks](https://mlsanity.com/paper/2212.07356/) (2022)
- [Wireless Quantized Federated Learning: A Joint Computation and Communication Design](https://mlsanity.com/paper/2203.05878/) (2022)
- [DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset](https://mlsanity.com/paper/2310.14906/) (2023)
- [Communication Efficient Federated Learning with Energy Awareness over Wireless Networks](https://mlsanity.com/paper/2004.07351/) (2020)
- [Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learning](https://mlsanity.com/paper/2106.12561/) (2021)
- [A Contribution-based Device Selection Scheme in Federated Learning](https://mlsanity.com/paper/2203.05369/) (2022)
- [Optimization-Based GenQSGD for Federated Edge Learning](https://mlsanity.com/paper/2110.12987/) (2021)
- [Energy Minimization for Federated Asynchronous Learning on Battery-Powered Mobile Devices via Application Co-running](https://mlsanity.com/paper/2204.13878/) (2022)
- [Dynamic Scheduling for Federated Edge Learning with Streaming Data](https://mlsanity.com/paper/2305.01238/) (2023)
- [Simultaneous Wireless Information and Power Transfer for Federated Learning](https://mlsanity.com/paper/2104.12749/) (2021)
- [Resource-Efficient and Delay-Aware Federated Learning Design under Edge Heterogeneity](https://mlsanity.com/paper/2112.13926/) (2021)

## Built on by (most cited first, 6 of 6)

- [A Lyapunov Drift-Plus-Penalty Method Tailored for Reinforcement Learning with Queue Stability](https://mlsanity.com/paper/2506.04291/) (2025)
- [Time-Varying Optimization for Streaming Data Via Temporal Weighting](https://mlsanity.com/paper/2510.13052/) (2025)
- [A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning](https://mlsanity.com/paper/2410.07881/) (2024)
- [Decentralized Time-Varying Optimization for Streaming Data via Temporal Weighting](https://mlsanity.com/paper/2605.06971/) (2026)
- [Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Scheduling](https://mlsanity.com/paper/2511.11949/) (2025)
- [Distributed Optimization with Streaming Data: A Temporal Weighting Perspective](https://mlsanity.com/paper/2608.09565/) (2026)

## Abstract (arXiv)

Federated learning (FL) has received significant attention in recent years for its advantages in efficient training of machine learning models across distributed clients without disclosing user-sensitive data. Specifically, in federated edge learning (FEEL) systems, the time-varying nature of wireless channels introduces inevitable system dynamics in the communication process, thereby affecting training latency and energy consumption. In this work, we further consider a streaming data scenario where new training data samples are randomly generated over time at edge devices. Our goal is to develop a dynamic scheduling and resource allocation algorithm to address the inherent randomness in data arrivals and resource availability under long-term energy constraints. To achieve this, we formulate a stochastic network optimization problem and use the Lyapunov drift-plus-penalty framework to obtain a dynamic resource management design. Our proposed algorithm makes adaptive decisions on device scheduling, computational capacity adjustment, and allocation of bandwidth and transmit power in every round. We provide convergence analysis for the considered setting with heterogeneous data and time-varying objective functions, which supports the rationale behind our proposed scheduling design. The effectiveness of our scheme is verified through simulation results, demonstrating improved learning performance and energy efficiency as compared to baseline schemes.