# Context-Aware Orchestration of Energy-Efficient Gossip Learning Schemes

arXiv: https://arxiv.org/abs/2404.12023 · HTML: https://mlsanity.com/paper/2404.12023/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 18 Apr 2024.

## Builds on (most cited first, 8 of 8)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Fast-Convergent Federated Learning with Adaptive Weighting](https://mlsanity.com/paper/2012.00661/) (2020)
- [Personalized Federated Learning through Local Memorization](https://mlsanity.com/paper/2111.09360/) (2021)
- [Communication-Efficient Federated Learning with Compensated Overlap-FedAvg](https://mlsanity.com/paper/2012.06706/) (2020)
- [Resource-Efficient Federated Learning](https://mlsanity.com/paper/2111.01108/) (2021)
- [FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robots](https://mlsanity.com/paper/2101.03705/) (2021)
- [Federated Learning at the Network Edge: When Not All Nodes are Created Equal](https://mlsanity.com/paper/2101.01995/) (2021)

## Built on by (most cited first, 1 of 1)

- [CARGO: Carbon-Aware Gossip Orchestration in Smart Shipping](https://mlsanity.com/paper/2603.27857/) (2026)

## Abstract (arXiv)

Fully distributed learning schemes such as Gossip Learning (GL) are gaining momentum due to their scalability and effectiveness even in dynamic settings. However, they often imply a high utilization of communication and computing resources, whose energy footprint may jeopardize the learning process, particularly on battery-operated IoT devices. To address this issue, we present Optimized Gossip Learning (OGL)}, a distributed training approach based on the combination of GL with adaptive optimization of the learning process, which allows for achieving a target accuracy while minimizing the energy consumption of the learning process. We propose a data-driven approach to OGL management that relies on optimizing in real-time for each node the number of training epochs and the choice of which model to exchange with neighbors based on patterns of node contacts, models' quality, and available resources at each node. Our approach employs a DNN model for dynamic tuning of the aforementioned parameters, trained by an infrastructure-based orchestrator function. We performed our assessments on two different datasets, leveraging time-varying random graphs and a measurement-based dynamic urban scenario. Results suggest that our approach is highly efficient and effective in a broad spectrum of network scenarios.