# Exploring the Impact of Serverless Computing on Peer To Peer Training Machine Learning

arXiv: https://arxiv.org/abs/2309.14139 · HTML: https://mlsanity.com/paper/2309.14139/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 45 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Builds on (most cited first, 20 of 20)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [A Survey on Distributed Machine Learning](https://mlsanity.com/paper/1912.09789/) (2019)
- [On Biased Compression for Distributed Learning](https://mlsanity.com/paper/2002.12410/) (2020)
- [BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning](https://mlsanity.com/paper/1905.06731/) (2019)
- [Collaborative Deep Learning in Fixed Topology Networks](https://mlsanity.com/paper/1706.07880/) (2017)
- [Personalized and Private Peer-to-Peer Machine Learning](https://mlsanity.com/paper/1705.08435/) (2017)
- [Effective Model Sparsification by Scheduled Grow-and-Prune Methods](https://mlsanity.com/paper/2106.09857/) (2021)
- [Distributed Learning of Deep Neural Networks using Independent Subnet Training](https://mlsanity.com/paper/1910.02120/) (2019)
- [Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selection](https://mlsanity.com/paper/2002.09692/) (2020)
- [Strategies and Principles of Distributed Machine Learning on Big Data](https://mlsanity.com/paper/1512.09295/) (2015)
- [Towards Demystifying Serverless Machine Learning Training](https://mlsanity.com/paper/2105.07806/) (2021)
- [FedLess: Secure and Scalable Federated Learning Using Serverless Computing](https://mlsanity.com/paper/2111.03396/) (2021)
- [Serverless Computing: A Survey of Opportunities, Challenges and Applications](https://mlsanity.com/paper/1911.01296/) (2019)
- [Performance Analysis and Comparison of Distributed Machine Learning Systems](https://mlsanity.com/paper/1909.02061/) (2019)
- [Communication-efficient Decentralized Machine Learning over Heterogeneous Networks](https://mlsanity.com/paper/2009.05766/) (2020)
- [SMLT: A Serverless Framework for Scalable and Adaptive Machine Learning Design and Training](https://mlsanity.com/paper/2205.01853/) (2022)
- [Stratum: A Serverless Framework for Lifecycle Management of Machine Learning based Data Analytics Tasks](https://mlsanity.com/paper/1904.01727/) (2019)
- [MLLess: Achieving Cost Efficiency in Serverless Machine Learning Training](https://mlsanity.com/paper/2206.05786/) (2022)
- [Sparse Deep Neural Network Exact Solutions](https://mlsanity.com/paper/1807.03165/) (2018)

## Built on by (most cited first, 3 of 3)

- [Scalable and Cost-Efficient ML Inference: Parallel Batch Processing with Serverless Functions](https://mlsanity.com/paper/2502.12017/) (2025)
- [Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning](https://mlsanity.com/paper/2604.22072/) (2026)
- [Event-Driven ML Pipeline Orchestration for Manufacturing: An AWS Industry Experience](https://mlsanity.com/paper/2610.06890/) (2026)

## Abstract (arXiv)

The increasing demand for computational power in big data and machine learning has driven the development of distributed training methodologies. Among these, peer-to-peer (P2P) networks provide advantages such as enhanced scalability and fault tolerance. However, they also encounter challenges related to resource consumption, costs, and communication overhead as the number of participating peers grows. In this paper, we introduce a novel architecture that combines serverless computing with P2P networks for distributed training and present a method for efficient parallel gradient computation under resource constraints. Our findings show a significant enhancement in gradient computation time, with up to a 97.34% improvement compared to conventional P2P distributed training methods. As for costs, our examination confirmed that the serverless architecture could incur higher expenses, reaching up to 5.4 times more than instance-based architectures. It is essential to consider that these higher costs are associated with marked improvements in computation time, particularly under resource-constrained scenarios. Despite the cost-time trade-off, the serverless approach still holds promise due to its pay-as-you-go model. Utilizing dynamic resource allocation, it enables faster training times and optimized resource utilization, making it a promising candidate for a wide range of machine learning applications.