# AutoDFL: A Scalable and Automated Reputation-Aware Decentralized Federated Learning

arXiv: https://arxiv.org/abs/2501.04331 · HTML: https://mlsanity.com/paper/2501.04331/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 8 Jan 2025.

## Builds on (most cited first, 10 of 10)

- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark](https://mlsanity.com/paper/2311.06750/) (2023)
- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [IPFS - Content Addressed, Versioned, P2P File System](https://mlsanity.com/paper/1407.3561/) (2014)
- [Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System](https://mlsanity.com/paper/2111.06290/) (2021)
- [Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learning](https://mlsanity.com/paper/2310.00222/) (2023)
- [HotStuff: BFT Consensus in the Lens of Blockchain](https://mlsanity.com/paper/1803.05069/) (2018)
- [ScaleSFL: A Sharding Solution for Blockchain-Based Federated Learning](https://mlsanity.com/paper/2204.01202/) (2022)
- [RollupTheCrowd: Leveraging ZkRollups for a Scalable and Privacy-Preserving Reputation-based Crowdsourcing Platform](https://mlsanity.com/paper/2407.02226/) (2024)

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

- [SettleFL: Trustless and Scalable Reward Settlement Protocol for Federated Learning on Permissionless Blockchains (Extended version)](https://mlsanity.com/paper/2602.23167/) (2026)
- [Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs](https://mlsanity.com/paper/2605.21115/) (2026)
- [Robust Reputation-Driven Crowdsourced Federated Learning](https://mlsanity.com/paper/2608.08574/) (2026)

## Abstract (arXiv)

Blockchained federated learning (BFL) combines the concepts of federated learning and blockchain technology to enhance privacy, security, and transparency in collaborative machine learning models. However, implementing BFL frameworks poses challenges in terms of scalability and cost-effectiveness. Reputation-aware BFL poses even more challenges, as blockchain validators are tasked with processing federated learning transactions along with the transactions that evaluate FL tasks and aggregate reputations. This leads to faster blockchain congestion and performance degradation. To improve BFL efficiency while increasing scalability and reducing on-chain reputation management costs, this paper proposes AutoDFL, a scalable and automated reputation-aware decentralized federated learning framework. AutoDFL leverages zk-Rollups as a Layer-2 scaling solution to boost the performance while maintaining the same level of security as the underlying Layer-1 blockchain. Moreover, AutoDFL introduces an automated and fair reputation model designed to incentivize federated learning actors. We develop a proof of concept for our framework for an accurate evaluation. Tested with various custom workloads, AutoDFL reaches an average throughput of over 3000 TPS with a gas reduction of up to 20X.