# Communication-Efficient and Interoperable Distributed Learning

arXiv: https://arxiv.org/abs/2509.22823 · HTML: https://mlsanity.com/paper/2509.22823/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 17 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Deep Learning for Classical Japanese Literature](https://mlsanity.com/paper/1812.01718/) (2018)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
- [The Road Towards 6G: A Comprehensive Survey](https://mlsanity.com/paper/2102.01420/) (2021)
- [A Bargaining Game for Personalized, Energy Efficient Split Learning over Wireless Networks](https://mlsanity.com/paper/2212.06107/) (2022)
- [Communication-Efficient Split Learning Based on Analog Communication and Over the Air Aggregation](https://mlsanity.com/paper/2106.00999/) (2021)
- [Collaborative Split Federated Learning with Parallel Training and Aggregation](https://mlsanity.com/paper/2504.15724/) (2025)
- [P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devices](https://mlsanity.com/paper/2507.17228/) (2025)

## Abstract (arXiv)

Collaborative learning across heterogeneous model architectures presents significant challenges in ensuring interoperability and preserving privacy. We propose a communication-efficient distributed learning framework that supports model heterogeneity and enables modular composition during inference. To facilitate interoperability, all clients adopt a common fusion-layer output dimension, which permits each model to be partitioned into a personalized base block and a generalized modular block. Clients share their fusion-layer outputs, keeping model parameters and architectures private. Experimental results demonstrate that the framework achieves superior communication efficiency compared to federated learning (FL) and federated split learning (FSL) baselines, while ensuring stable training performance across heterogeneous architectures.