# FedAvgen: Metadata for Model Aggregation In Communication Systems

arXiv: https://arxiv.org/abs/2505.05486 · HTML: https://mlsanity.com/paper/2505.05486/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 28 Apr 2025.

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Revisiting Distributed Synchronous SGD](https://mlsanity.com/paper/1702.05800/) (2017)
- [Papaya: Practical, Private, and Scalable Federated Learning](https://mlsanity.com/paper/2111.04877/) (2021)
- [Fast-Convergent Federated Learning with Adaptive Weighting](https://mlsanity.com/paper/2012.00661/) (2020)
- [Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations](https://mlsanity.com/paper/2103.10481/) (2021)
- [Asynchronous Decentralized SGD with Quantized and Local Updates](https://mlsanity.com/paper/1910.12308/) (2019)
- [Dynamic Fusion based Federated Learning for COVID-19 Detection](https://mlsanity.com/paper/2009.10401/) (2020)
- [A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison](https://mlsanity.com/paper/2110.10223/) (2021)
- [Checkpoint Merging via Bayesian Optimization in LLM Pretraining](https://mlsanity.com/paper/2403.19390/) (2024)
- [Optimizing the Collaboration Structure in Cross-Silo Federated Learning](https://mlsanity.com/paper/2306.06508/) (2023)
- [Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data](https://mlsanity.com/paper/2104.12678/) (2021)
- [Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning](https://mlsanity.com/paper/2107.11415/) (2021)
- [Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback](https://mlsanity.com/paper/2112.14332/) (2021)
- [Asynchronous Semi-Decentralized Federated Edge Learning for Heterogeneous Clients](https://mlsanity.com/paper/2112.04737/) (2021)

## Abstract (arXiv)

To improve business efficiency and minimize costs, Artificial Intelligence (AI) practitioners have adopted a shift from formulating models from scratch towards sharing pretrained models. The pretrained models are then aggregated into a global model with higher generalization capabilities, which is afterwards distributed to the client devices. This approach is known as federated learning and inherently utilizes different techniques to select the candidate client models averaged to obtain the global model. This approach, in the case of communication systems, faces challenges arising from the existential diversity in device profiles. The multiplicity in profiles motivates our conceptual assessment of a metaheuristic algorithm (FedAvgen), which relates each pretrained model with its weight space as metadata, to a phenotype and genotype, respectively. This parent-child genetic evolution characterizes the global averaging step in federated learning. We then compare the results of our approach to two widely adopted baseline federated learning algorithms like Federated Averaging (FedAvg) and Federated Stochastic Gradient Descent (FedSGD).