# Feature-Based Semantics-Aware Scheduling for Energy-Harvesting Federated Learning

arXiv: https://arxiv.org/abs/2512.01983 · HTML: https://mlsanity.com/paper/2512.01983/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 1 Dec 2025.

## Builds on (most cited first, 17 of 17)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Federated Multi-Task Learning under a Mixture of Distributions](https://mlsanity.com/paper/2108.10252/) (2021)
- [FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning](https://mlsanity.com/paper/2103.13822/) (2021)
- [Personalized Decentralized Federated Learning with Knowledge Distillation](https://mlsanity.com/paper/2302.12156/) (2023)
- [The Age of Gossip in Networks](https://mlsanity.com/paper/2102.02893/) (2021)
- [Version age-based client scheduling policy for federated learning](https://mlsanity.com/paper/2402.05407/) (2024)
- [Semantic-Aware Remote Estimation of Multiple Markov Sources Under Constraints](https://mlsanity.com/paper/2403.16855/) (2024)
- [Version Age of Information in Clustered Gossip Networks](https://mlsanity.com/paper/2109.08669/) (2021)
- [Measuring Data Similarity for Efficient Federated Learning: A Feasibility Study](https://mlsanity.com/paper/2403.07450/) (2024)
- [Version Age-Optimal Cached Status Updates in a Gossiping Network with Energy Harvesting Sensor](https://mlsanity.com/paper/2311.01909/) (2023)
- [Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning](https://mlsanity.com/paper/2504.12181/) (2025)
- [Federated Learning-Distillation Alternation for Resource-Constrained IoT](https://mlsanity.com/paper/2505.20456/) (2025)
- [Semantics-Aware Updates from Remote Energy Harvesting Devices to Interconnected LEO Satellites](https://mlsanity.com/paper/2502.07069/) (2025)

## Abstract (arXiv)

Federated Learning (FL) on resource-constrained edge devices faces a critical challenge: The computational energy required for training Deep Neural Networks (DNNs) often dominates communication costs. However, most existing Energy-Harvesting FL (EHFL) strategies fail to account for this reality, resulting in wasted energy due to redundant local computations. For efficient and proactive resource management, algorithms that predict local update contributions must be devised. We propose a lightweight client scheduling framework using the Version Age of Information (VAoI), a semantics-aware metric that quantifies update timeliness and significance. Crucially, we overcome VAoI's typical prohibitive computational cost, which requires statistical distance over the entire parameter space, by introducing a feature-based proxy. This proxy estimates model redundancy using intermediate-layer extraction from a single forward pass, dramatically reducing computational complexity. Experiments conducted under extreme non-IID data distributions and scarce energy availability demonstrate superior learning performance while achieving energy reduction compared to existing baseline selection policies. Our framework establishes semantics-aware scheduling as a practical and vital solution for EHFL in realistic scenarios where training costs dominate transmission costs.