# A Survey on Model-heterogeneous Federated Learning: Problems, Methods, and Prospects

arXiv: https://arxiv.org/abs/2312.12091 · HTML: https://mlsanity.com/paper/2312.12091/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2023.

## Built on by (most cited first, 9 of 9)

- [ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learning](https://mlsanity.com/paper/2603.04437/) (2026)
- [CRAD: Class-wise Reliability-Aware Distillation for Decentralized Heterogeneous Federated Learning](https://mlsanity.com/paper/2609.00446/) (2026)
- [FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning](https://mlsanity.com/paper/2407.05098/) (2024)
- [FedVSR: Towards Model-Agnostic Federated Learning in Video Super-Resolution](https://mlsanity.com/paper/2503.13745/) (2025)
- [Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learning](https://mlsanity.com/paper/2511.00655/) (2025)
- [QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition](https://mlsanity.com/paper/2607.02426/) (2026)
- [CALM: Class-wise Agreement and Label-gated Disagreement Modulation for Decentralized Federated Learning](https://mlsanity.com/paper/2609.05884/) (2026)
- [Decentralized Gossip Learning and Federated Averaging for Histopathology Image Classification](https://mlsanity.com/paper/2609.16448/) (2026)
- [Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentation](https://mlsanity.com/paper/2610.03474/) (2026)

## Abstract (arXiv)

As privacy concerns continue to grow, federated learning (FL) has gained significant attention as a promising privacy-preserving technology, leading to considerable advancements in recent years. Unlike traditional machine learning, which requires central data collection, FL keeps data localized on user devices. However, conventional FL assumes that all clients operate with identical model structures initialized by the server. In real-world applications, system heterogeneity is common, with clients possessing varying computational capabilities. This disparity can hinder training for resource-limited clients and result in inefficient resource use for those with greater processing power. To address this challenge, model-heterogeneous FL has been introduced, enabling clients to train models of varying complexity based on their hardware resources. This paper reviews state-of-the-art approaches in model-heterogeneous FL, analyzing their strengths and weaknesses, while identifying open challenges and future research directions. To the best of our knowledge, this is the first survey to specifically focus on model-heterogeneous FL.