TransferFed: A Unified Framework for Blockchain-Secured, Privacy-Preserving, and Incentive-Aware Federated Learning

Federated learning (FL) enables decentralized model training but faces persistent challenges from client data heterogeneity, potential privacy leaks, and a lack of incentives for participation. This paper introduces TransferFed, a novel and comprehensive framework designed to address these interconnected issues. The primary objective of TransferFed is to create a robust, secure, and efficient FL ecosystem. To achieve this, our proposed framework synergistically integrates four key components: (i) Transfer learning (TL) based initialization, which uses publicly pretrained models to warm-start client training, accelerating convergence and mitigating issues from non-IID data; (ii) A dynamic, adaptive aggregation mechanism that scores and weights client contributions based on utility gain and historical reliability, filtering out low-quality or anomalous updates; (iii) An adversarially-trained privacy bottleneck that enforces rigorous, information-theoretic privacy bounds on client data by minimizing mutual information leakage; and (iv) A dual-layer blockchain architecture that provides a secure and cost-efficient backbone for model provenance and incentive distribution. The research findings presented are theoretical, outlining a system that achieves provable convergence rates, formal privacy guarantees, and an honest-Nash equilibrium for its token-based economy. TransferFed establishes a deployable, incentive-compatible blueprint for real-world, TL-enhanced FL.