Communication-Efficient and Interoperable Distributed Learning
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915517119332352 |
|---|---|
| author | Krouka, Mounssif Bennis, Mehdi |
| author_facet | Krouka, Mounssif Bennis, Mehdi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22823 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Communication-Efficient and Interoperable Distributed Learning Krouka, Mounssif Bennis, Mehdi Machine Learning 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. |
| title | Communication-Efficient and Interoperable Distributed Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.22823 |