PureFed: An Efficient Collaborative and Trustworthy Federated Learning Framework Based on Blockchain Network - Made Adi Paramartha Putra; Nyoman Bogi Aditya Karna; Revin Naufal Alief; Ahmad Zainudin; Dong-Seong Kim; Jae-Min Lee; Gabriel Avelino Sampedro (IEEE Access, 2024)
This paper introduces PureFed, an innovative Federated Learning (FL) framework designed for efficiency, collaboration, and trustworthiness. In the background of FL research, it was observed that previous frameworks often neglected participant privacy, a critical aspect not aligned with the core FL concept. Additionally, there was room for improving the efficiency of existing frameworks. PureFed addresses these shortcomings by offering participants the flexibility to initiate FL tasks or join existing ones without sharing any private data and removing unnecessary actions that led to an inefficient system. Leveraging blockchain technology, it employs smart contracts to ensure traceability and immutability, enhancing the security of the framework. Additionally, PureFed employs symmetric key encryption and dual digital signature mechanisms using ECDSA to guarantee the confidentiality and integrity of shared models. To expedite model convergence, PureFed incorporates a dynamic aggregation scheme, selecting the most suitable model from three distinct techniques: FedAvg, accuracy-based, and loss-based. Furthermore, the framework introduces a dynamic incentive and punishment mechanism to incentivize collaboration and maintain trust. Extensive performance evaluations reveal PureFed’s significant advantages. It outperforms its counterparts by 63.39% and 67.72% in terms of smart contract deployment and interaction gas costs, respectively. Lastly, scalability analyses indicate PureFed’s ability to adapt efficiently, achieving target accuracy in fewer rounds.