Federated Learning Over Blockchain for Collaborative AI Training
DOI:
https://doi.org/10.63345/sjaibt.v2.i2.303Keywords:
Federated Learning, Blockchain, Collaborative AI, Smart Contracts, Privacy-Preserving Machine Learning, Decentralized AI, Data IntegrityAbstract
Federated Learning (FL) has emerged as a decentralized machine learning paradigm that enables multiple participants to collaboratively train models without directly sharing their sensitive local data. This paradigm addresses privacy concerns while allowing scalable model development across distributed data silos. However, traditional FL architectures still suffer from critical limitations: trust issues among participants, susceptibility to single-point failures, and vulnerabilities in maintaining data and model update integrity. Blockchain, with its immutable ledger, decentralized consensus, and incentive mechanisms, offers a promising infrastructure to overcome these challenges.
This manuscript explores the integration of blockchain into federated learning frameworks to establish a trustworthy, transparent, and collaborative ecosystem for Artificial Intelligence (AI) training. The study begins with an extensive literature review on FL architectures, blockchain frameworks, and their synergies. It then proposes a blockchain-enhanced federated learning methodology that employs smart contracts for model aggregation, token-based incentives for honest participation, and decentralized consensus to ensure tamper-proof recording of model updates. A detailed methodology is presented, emphasizing architecture, communication protocols, cryptographic primitives, and consensus mechanisms.
Experimental simulations and comparative evaluations demonstrate that blockchain-enabled FL improves robustness, transparency, and fairness, while maintaining privacy guarantees. Results highlight improvements in trustworthiness of updates, reduced vulnerability to poisoning attacks, and enhanced auditability across participating nodes. However, challenges remain, particularly in terms of scalability, latency, and energy efficiency.
The manuscript concludes that blockchain-integrated FL represents a transformative step toward secure collaborative AI training, particularly relevant in healthcare, finance, and smart cities. Future research must focus on lightweight blockchain protocols, energy-efficient consensus, and adaptive incentive mechanisms to enable large-scale adoption.
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