# Distributed Learning and Inference Systems: A Networking Perspective

arXiv: https://arxiv.org/abs/2501.05323 · HTML: https://mlsanity.com/paper/2501.05323/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 9 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [A Survey on Distributed Machine Learning](https://mlsanity.com/paper/1912.09789/) (2019)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Continual Learning and Catastrophic Forgetting](https://mlsanity.com/paper/2403.05175/) (2024)
- [Decentralized Federated Learning: A Segmented Gossip Approach](https://mlsanity.com/paper/1908.07782/) (2019)
- [The Effect of Task Ordering in Continual Learning](https://mlsanity.com/paper/2205.13323/) (2022)
- [Collaborative Inference for AI-Empowered IoT Devices](https://mlsanity.com/paper/2207.11664/) (2022)
- [Tram-FL: Routing-based Model Training for Decentralized Federated Learning](https://mlsanity.com/paper/2308.04762/) (2023)

## Built on by (most cited first, 1 of 1)

- [An Invariant Latent Space Perspective on Language Model Inversion](https://mlsanity.com/paper/2511.19569/) (2025)

## Abstract (arXiv)

Machine learning models have achieved, and in some cases surpassed, human-level performance in various tasks, mainly through centralized training of static models and the use of large models stored in centralized clouds for inference. However, this centralized approach has several drawbacks, including privacy concerns, high storage demands, a single point of failure, and significant computing requirements. These challenges have driven interest in developing alternative decentralized and distributed methods for AI training and inference. Distribution introduces additional complexity, as it requires managing multiple moving parts. To address these complexities and fill a gap in the development of distributed AI systems, this work proposes a novel framework, Data and Dynamics-Aware Inference and Training Networks (DA-ITN). The different components of DA-ITN and their functions are explored, and the associated challenges and research areas are highlighted.