# Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning

arXiv: https://arxiv.org/abs/1811.09904 · HTML: https://mlsanity.com/paper/1811.09904/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2018.

## Built on by (most cited first, 17 of 17)

- [FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robots](https://mlsanity.com/paper/2101.03705/) (2021)
- [Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review](https://mlsanity.com/paper/2205.07855/) (2022)
- [FL-Market: Trading Private Models in Federated Learning](https://mlsanity.com/paper/2106.04384/) (2021)
- [BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning](https://mlsanity.com/paper/2202.02817/) (2022)
- [A Survey of Federated Evaluation in Federated Learning](https://mlsanity.com/paper/2305.08070/) (2023)
- [Implicit Model Specialization through DAG-based Decentralized Federated Learning](https://mlsanity.com/paper/2111.01257/) (2021)
- [Technical Report: Assisting Backdoor Federated Learning with Whole Population Knowledge Alignment](https://mlsanity.com/paper/2207.12327/) (2022)
- [P4L: Privacy Preserving Peer-to-Peer Learning for Infrastructureless Setups](https://mlsanity.com/paper/2302.13438/) (2023)
- [SPIRT: A Fault-Tolerant and Reliable Peer-to-Peer Serverless ML Training Architecture](https://mlsanity.com/paper/2309.14148/) (2023)
- [IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Content](https://mlsanity.com/paper/2406.08526/) (2024)
- [Reward-Based 1-bit Compressed Federated Distillation on Blockchain](https://mlsanity.com/paper/2106.14265/) (2021)
- [Clustering Algorithm to Detect Adversaries in Federated Learning](https://mlsanity.com/paper/2102.10799/) (2021)
- [DearFSAC: An Approach to Optimizing Unreliable Federated Learning via Deep Reinforcement Learning](https://mlsanity.com/paper/2201.12701/) (2022)
- [Over-The-Air Clustered Wireless Federated Learning](https://mlsanity.com/paper/2211.03363/) (2022)
- [Robust and Actively Secure Serverless Collaborative Learning](https://mlsanity.com/paper/2310.16678/) (2023)
- [Stackelberg Game Based Performance Optimization in Digital Twin Assisted Federated Learning over NOMA Networks](https://mlsanity.com/paper/2501.01584/) (2025)
- [Gradients: When Markets Meet Fine-tuning -- A Distributed Approach to Model Optimisation](https://mlsanity.com/paper/2506.07940/) (2025)

## Abstract (arXiv)

Federated Learning is the current state of the art in supporting secure multi-party machine learning (ML): data is maintained on the owner's device and the updates to the model are aggregated through a secure protocol. However, this process assumes a trusted centralized infrastructure for coordination, and clients must trust that the central service does not use the byproducts of client data. In addition to this, a group of malicious clients could also harm the performance of the model by carrying out a poisoning attack. As a response, we propose Biscotti: a fully decentralized peer to peer (P2P) approach to multi-party ML, which uses blockchain and cryptographic primitives to coordinate a privacy-preserving ML process between peering clients. Our evaluation demonstrates that Biscotti is scalable, fault tolerant, and defends against known attacks. For example, Biscotti is able to protect the privacy of an individual client's update and the performance of the global model at scale when 30% of adversaries are trying to poison the model. The implementation can be found at: https://github.com/DistributedML/Biscotti