# Heterogeneous Federated Learning

arXiv: https://arxiv.org/abs/2008.06767 · HTML: https://mlsanity.com/paper/2008.06767/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2020.

## Built on by (most cited first, 19 of 19)

- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Multi-Center Federated Learning: Clients Clustering for Better Personalization](https://mlsanity.com/paper/2108.08647/) (2021)
- [Federated Domain Generalization: A Survey](https://mlsanity.com/paper/2306.01334/) (2023)
- [Resilient Constrained Learning](https://mlsanity.com/paper/2306.02426/) (2023)
- [CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning](https://mlsanity.com/paper/2204.01542/) (2022)
- [QFAL: Quantum Federated Adversarial Learning](https://mlsanity.com/paper/2502.21171/) (2025)
- [Contrastive encoder pre-training-based clustered federated learning for heterogeneous data](https://mlsanity.com/paper/2311.16535/) (2023)
- [Secure Multi-Party Computation based Privacy Preserving Data Analysis in Healthcare IoT Systems](https://mlsanity.com/paper/2109.14334/) (2021)
- [Adversarial Robustness in Distributed Quantum Machine Learning](https://mlsanity.com/paper/2508.11848/) (2025)
- [Plankton-FL: Exploration of Federated Learning for Privacy-Preserving Training of Deep Neural Networks for Phytoplankton Classification](https://mlsanity.com/paper/2212.08990/) (2022)
- [Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices](https://mlsanity.com/paper/2411.07826/) (2024)
- [Generalized and Personalized Federated Learning with Black-Box Foundation Models via Orthogonal Transformations](https://mlsanity.com/paper/2505.19888/) (2025)
- [Decoding Federated Learning: The FedNAM+ Conformal Revolution](https://mlsanity.com/paper/2506.17872/) (2025)
- [FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection](https://mlsanity.com/paper/2604.24012/) (2026)
- [Subspace Optimization for Efficient Federated Learning under Heterogeneous Data](https://mlsanity.com/paper/2604.25467/) (2026)
- [Differential Privacy: What is all the noise about?](https://mlsanity.com/paper/2205.09453/) (2022)
- [Evaluating Multi-Global Server Architecture for Federated Learning](https://mlsanity.com/paper/2311.15382/) (2023)
- [DART: A Server-side Plug-in for Resource-efficient Robust Federated Learning](https://mlsanity.com/paper/2508.17381/) (2025)
- [CoGraM: Context-sensitive granular optimization method with rollback for robust model fusion](https://mlsanity.com/paper/2512.03610/) (2025)

## Abstract (arXiv)

Federated learning learns from scattered data by fusing collaborative models from local nodes. However, due to chaotic information distribution, the model fusion may suffer from structural misalignment with regard to unmatched parameters. In this work, we propose a novel federated learning framework to resolve this issue by establishing a firm structure-information alignment across collaborative models. Specifically, we design a feature-oriented regulation method (Psi-Net) to ensure explicit feature information allocation in different neural network structures. Applying this regulating method to collaborative models, matchable structures with similar feature information can be initialized at the very early training stage. During the federated learning process under either IID or non-IID scenarios, dedicated collaboration schemes further guarantee ordered information distribution with definite structure matching, so as the comprehensive model alignment. Eventually, this framework effectively enhances the federated learning applicability to extensive heterogeneous settings, while providing excellent convergence speed, accuracy, and computation/communication efficiency.