# Connectivity-Aware Semi-Decentralized Federated Learning over Time-Varying D2D Networks

arXiv: https://arxiv.org/abs/2303.08988 · HTML: https://mlsanity.com/paper/2303.08988/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2023.

## Built on by (most cited first, 13 of 13)

- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data](https://mlsanity.com/paper/2501.09822/) (2025)
- [Communication-Efficient Federated Optimization over Semi-Decentralized Networks](https://mlsanity.com/paper/2311.18787/) (2023)
- [Robust Collaborative Inference with Vertically Split Data Over Dynamic Device Environments](https://mlsanity.com/paper/2312.16638/) (2023)
- [Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation](https://mlsanity.com/paper/2401.07456/) (2024)
- [The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions](https://mlsanity.com/paper/2501.11714/) (2025)
- [FedDec: Peer-to-peer Aided Federated Learning](https://mlsanity.com/paper/2306.06715/) (2023)
- [TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning](https://mlsanity.com/paper/2412.11448/) (2024)
- [Decentralized Domain Generalization with Style Sharing: Formal Model and Convergence Analysis](https://mlsanity.com/paper/2504.06235/) (2025)
- [How to Evaluate Participant Contributions in Decentralized Federated Learning](https://mlsanity.com/paper/2505.23246/) (2025)
- [From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walks](https://mlsanity.com/paper/2509.03709/) (2025)
- [Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges](https://mlsanity.com/paper/2509.07946/) (2025)

## Abstract (arXiv)

Semi-decentralized federated learning blends the conventional device to-server (D2S) interaction structure of federated model training with localized device-to-device (D2D) communications. We study this architecture over practical edge networks with multiple D2D clusters modeled as time-varying and directed communication graphs. Our investigation results in an algorithm that controls the fundamental trade-off between (a) the rate of convergence of the model training process towards the global optimizer, and (b) the number of D2S transmissions required for global aggregation. Specifically, in our semi-decentralized methodology, D2D consensus updates are injected into the federated averaging framework based on column-stochastic weight matrices that encapsulate the connectivity within the clusters. To arrive at our algorithm, we show how the expected optimality gap in the current global model depends on the greatest two singular values of the weighted adjacency matrices (and hence on the densities) of the D2D clusters. We then derive tight bounds on these singular values in terms of the node degrees of the D2D clusters, and we use the resulting expressions to design a threshold on the number of clients required to participate in any given global aggregation round so as to ensure a desired convergence rate. Simulations performed on real-world datasets reveal that our connectivity-aware algorithm reduces the total communication cost required to reach a target accuracy significantly compared with baselines depending on the connectivity structure and the learning task.