# Convergence Analysis of Sequential Federated Learning on Heterogeneous Data

arXiv: https://arxiv.org/abs/2311.03154 · HTML: https://mlsanity.com/paper/2311.03154/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2023.

## Built on by (most cited first, 23 of 23)

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- [FedDES: Graph-Based Dynamic Ensemble Selection for Personalized Federated Learning](https://mlsanity.com/paper/2603.28006/) (2026)
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## Abstract (arXiv)

There are two categories of methods in Federated Learning (FL) for joint training across multiple clients: (i) parallel FL (PFL), where clients train models in a parallel manner; and (ii) sequential FL (SFL), where clients train models in a sequential manner. In contrast to that of PFL, the convergence theory of SFL on heterogeneous data is still lacking. In this paper, we establish the convergence guarantees of SFL for strongly/general/non-convex objectives on heterogeneous data. The convergence guarantees of SFL are better than that of PFL on heterogeneous data with both full and partial client participation. Experimental results validate the counterintuitive analysis result that SFL outperforms PFL on extremely heterogeneous data in cross-device settings.