Blockchain-Enabled Trustworthy AI: Decentralized Federated Learning with Proof-of-Learning Consensus

We introduce PoL-FL, a blockchain-based FL system where participants submit cryptographic proofs of valid training (Proof-of-Learning) to a smart contract on Ethereum L2 (Optimistic Rollups). PoL-FL combines ZK-STARKs for proving gradient correctness and sharded model aggregation to scale to 10,000 nodes. Tests on IoT sensor data show Byzantine fault tolerance (BFT) under 30% malicious nodes, with 12% lower energy than PoW-based FL.

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Publication Details

Journal
Journal of Information Systems Engineering & Management
Published
2025-05-07
DOI
https://doi.org/10.52783/jisem.v10i44s.8694
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
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article

Blockchain-Enabled Trustworthy AI: Decentralized Federated Learning with Proof-of-Learning Consensus

Deepak Kejriwal
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Journal of Information Systems Engineering & Management
Privacy-Preserving Technologies in Data
article

Blockchain-Enabled Trustworthy AI: Decentralized Federated Learning with Proof-of-Learning Consensus

Deepak Kejriwal
article en

Abstract

We introduce PoL-FL, a blockchain-based FL system where participants submit cryptographic proofs of valid training (Proof-of-Learning) to a smart contract on Ethereum L2 (Optimistic Rollups). PoL-FL combines ZK-STARKs for proving gradient correctness and sharded model aggregation to scale to 10,000 nodes. Tests on IoT sensor data show Byzantine fault tolerance (BFT) under 30% malicious nodes, with 12% lower energy than PoW-based FL.

Journal of Information Systems Engineering & ManagementVol. 10(44s)
Openalex Percentile: Top 95%
Privacy-Preserving Technologies in Data
DOIOpen Access PDFOpenAlex
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Blockchain-Enabled Trustworthy AI: Decentralized Federated Learning with Proof-of-Learning Consensus — Deepak Kejriwal · Journal of Information Systems Engineering & Management (2025) | TGRS Research Map | TGRS