# Proof of Federated Learning: A Novel Energy-recycling Consensus Algorithm

arXiv: https://arxiv.org/abs/1912.11745 · HTML: https://mlsanity.com/paper/1912.11745/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2019.

## Built on by (most cited first, 26 of 26)

- [Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation](https://mlsanity.com/paper/2101.06905/) (2021)
- [Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective](https://mlsanity.com/paper/2111.11850/) (2021)
- [A Survey on Decentralized Federated Learning](https://mlsanity.com/paper/2308.04604/) (2023)
- [Decentralized Federated Learning: A Survey on Security and Privacy](https://mlsanity.com/paper/2401.17319/) (2024)
- [Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review](https://mlsanity.com/paper/2205.07855/) (2022)
- [Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus](https://mlsanity.com/paper/2101.03300/) (2021)
- [Secure and Efficient Federated Learning Through Layering and Sharding Blockchain](https://mlsanity.com/paper/2104.13130/) (2021)
- [Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning](https://mlsanity.com/paper/2403.19178/) (2024)
- [A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainability](https://mlsanity.com/paper/2310.17944/) (2023)
- [Privacy-Preserving in Blockchain-based Federated Learning Systems](https://mlsanity.com/paper/2401.03552/) (2024)
- [FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Quality](https://mlsanity.com/paper/2401.07558/) (2024)
- [Faster Federated Learning with Decaying Number of Local SGD Steps](https://mlsanity.com/paper/2305.09628/) (2023)
- [FLEX: FLEXible Federated Learning Framework](https://mlsanity.com/paper/2404.06127/) (2024)
- [OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning](https://mlsanity.com/paper/2105.00602/) (2021)
- [Proof-of-Collaborative-Learning: A Multi-winner Federated Learning Consensus Algorithm](https://mlsanity.com/paper/2407.13018/) (2024)
- [Can Blockchains Reliably Train Machine Learning Models?](https://mlsanity.com/paper/2307.07066/) (2023)
- [Blockchain-Enabled Federated Learning](https://mlsanity.com/paper/2508.06406/) (2025)
- [A Collaboration Strategy in the Mining Pool for Proof-of-Neural-Architecture Consensus](https://mlsanity.com/paper/2206.07089/) (2022)
- [A Blockchain-empowered Multi-Aggregator Federated Learning Architecture in Edge Computing with Deep Reinforcement Learning Optimization](https://mlsanity.com/paper/2310.09665/) (2023)
- [A Quality-of-Service Compliance System using Federated Learning and Optimistic Rollups](https://mlsanity.com/paper/2312.00026/) (2023)
- [Proof-of-Data: A Consensus Protocol for Collaborative Intelligence](https://mlsanity.com/paper/2501.02971/) (2025)
- [From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0](https://mlsanity.com/paper/2507.15796/) (2025)
- [Enhancing Split Learning with Sharded and Blockchain-Enabled SplitFed Approaches](https://mlsanity.com/paper/2509.25555/) (2025)
- [SEDULity: A Proof-of-Learning Framework for Distributed and Secure Blockchains with Efficient Useful Work](https://mlsanity.com/paper/2512.13666/) (2025)
- [Resilient Federated Chain: Transforming Blockchain Consensus into an Active Defense Layer for Federated Learning](https://mlsanity.com/paper/2602.21841/) (2026)
- [DeRelayL: Sustainable Decentralized Relay Learning](https://mlsanity.com/paper/2605.02935/) (2026)

## Abstract (arXiv)

Proof of work (PoW), the most popular consensus mechanism for Blockchain, requires ridiculously large amounts of energy but without any useful outcome beyond determining accounting rights among miners. To tackle the drawback of PoW, we propose a novel energy-recycling consensus algorithm, namely proof of federated learning (PoFL), where the energy originally wasted to solve difficult but meaningless puzzles in PoW is reinvested to federated learning. Federated learning and pooled-ming, a trend of PoW, have a natural fit in terms of organization structure. However, the separation between the data usufruct and ownership in Blockchain lead to data privacy leakage in model training and verification, deviating from the original intention of federal learning. To address the challenge, a reverse game-based data trading mechanism and a privacy-preserving model verification mechanism are proposed. The former can guard against training data leakage while the latter verifies the accuracy of a trained model with privacy preservation of the task requester's test data as well as the pool's submitted model. To the best of our knowledge, our paper is the first work to employ federal learning as the proof of work for Blockchain. Extensive simulations based on synthetic and real-world data demonstrate the effectiveness and efficiency of our proposed mechanisms.