# Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees

arXiv: https://arxiv.org/abs/2402.03448 · HTML: https://mlsanity.com/paper/2402.03448/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2024.

## Built on by (most cited first, 10 of 10)

- [DICE: Data Influence Cascade in Decentralized Learning](https://mlsanity.com/paper/2507.06931/) (2025)
- [ERIS: Enhancing the Privacy-Utility-Scalability Trade-Off in Federated Learning via Federated Shard Aggregation](https://mlsanity.com/paper/2602.08617/) (2026)
- [Decentralized Domain Generalization with Style Sharing: Formal Model and Convergence Analysis](https://mlsanity.com/paper/2504.06235/) (2025)
- [Sporadic Gradient Tracking over Directed Graphs: A Theoretical Perspective on Decentralized Federated Learning](https://mlsanity.com/paper/2602.00791/) (2026)
- [On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approach](https://mlsanity.com/paper/2605.26162/) (2026)
- [Towards Serverless Semi-Decentralized Federated Learning with Heterogeneous Optimizers](https://mlsanity.com/paper/2606.06687/) (2026)
- [From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning](https://mlsanity.com/paper/2606.10595/) (2026)
- [TallyTrain: Communication-Efficient Federated Distillation](https://mlsanity.com/paper/2607.00173/) (2026)
- [Federated Lightweight Fine-Tuning](https://mlsanity.com/paper/2607.18343/) (2026)
- [SPADE-DFL: Communication-Efficient Decentralized Federated Learning via Derivative-Free Linearized ADMM](https://mlsanity.com/paper/2609.29446/) (2026)

## Abstract (arXiv)

Decentralized federated learning (DFL) captures FL settings where both (i) model updates and (ii) model aggregations are exclusively carried out by the clients without a central server. Existing DFL works have mostly focused on settings where clients conduct a fixed number of local updates between local model exchanges, overlooking heterogeneity and dynamics in communication and computation capabilities. In this work, we propose Decentralized Sporadic Federated Learning (textttDSpodFL), a DFL methodology built on a generalized notion of textitsporadicity in both local gradient and aggregation processes. textttDSpodFL subsumes many existing decentralized optimization methods under a unified algorithmic framework by modeling the per-iteration (i) occurrence of gradient descent at each client and (ii) exchange of models between client pairs as arbitrary indicator random variables, thus capturing textitheterogeneous and time-varying computation/communication scenarios. We analytically characterize the convergence behavior of textttDSpodFL for both convex and non-convex models and for both constant and diminishing learning rates, under mild assumptions on the communication graph connectivity, data heterogeneity across clients, and gradient noises. We show how our bounds recover existing results from decentralized gradient descent as special cases. Experiments demonstrate that textttDSpodFL consistently achieves improved training speeds compared with baselines under various system settings.