# SPIRT: A Fault-Tolerant and Reliable Peer-to-Peer Serverless ML Training Architecture

arXiv: https://arxiv.org/abs/2309.14148 · HTML: https://mlsanity.com/paper/2309.14148/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 62 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A Survey on Distributed Machine Learning](https://mlsanity.com/paper/1912.09789/) (2019)
- [RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets](https://mlsanity.com/paper/1811.03761/) (2018)
- [Decentralized Federated Averaging](https://mlsanity.com/paper/2104.11375/) (2021)
- [Generalized Byzantine-tolerant SGD](https://mlsanity.com/paper/1802.10116/) (2018)
- [Learning to Detect Malicious Clients for Robust Federated Learning](https://mlsanity.com/paper/2002.00211/) (2020)
- [BRIDGE: Byzantine-resilient Decentralized Gradient Descent](https://mlsanity.com/paper/1908.08098/) (2019)
- [Distributed Learning of Deep Neural Networks using Independent Subnet Training](https://mlsanity.com/paper/1910.02120/) (2019)
- [Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning](https://mlsanity.com/paper/1811.09904/) (2018)
- [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)
- [Performance Analysis and Comparison of Distributed Machine Learning Systems](https://mlsanity.com/paper/1909.02061/) (2019)
- [Fault Tolerance in Iterative-Convergent Machine Learning](https://mlsanity.com/paper/1810.07354/) (2018)
- [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)

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

- [Matrix: Peer-to-Peer Multi-Agent Synthetic Data Generation Framework](https://mlsanity.com/paper/2511.21686/) (2025)
- [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)

## Abstract (arXiv)

The advent of serverless computing has ushered in notable advancements in distributed machine learning, particularly within parameter server-based architectures. Yet, the integration of serverless features within peer-to-peer (P2P) distributed networks remains largely uncharted. In this paper, we introduce SPIRT, a fault-tolerant, reliable, and secure serverless P2P ML training architecture. designed to bridge this existing gap. Capitalizing on the inherent robustness and reliability innate to P2P systems, SPIRT employs RedisAI for in-database operations, leading to an 82% reduction in the time required for model updates and gradient averaging across a variety of models and batch sizes. This architecture showcases resilience against peer failures and adeptly manages the integration of new peers, thereby highlighting its fault-tolerant characteristics and scalability. Furthermore, SPIRT ensures secure communication between peers, enhancing the reliability of distributed machine learning tasks. Even in the face of Byzantine attacks, the system's robust aggregation algorithms maintain high levels of accuracy. These findings illuminate the promising potential of serverless architectures in P2P distributed machine learning, offering a significant stride towards the development of more efficient, scalable, and resilient applications.