Enhancing SCF with Privacy-Preserving and Splitting-Enabled E-Bills on Blockchain

Electronic Bill (E-Bill) is a rucial negotiable instrument in the form of data messages, relying on the Electronic Bill System (EB System). Blockchain technology offers inherent data sharing capabilities, so it is increasingly being adopted by small and medium-sized enterprises (SMEs) in the supply chain to build EB systems. However, the blockchain-based E-Bill still face significant challenges: the E-Bill is difficult to split, like non-fungible tokens (NFTs), and sensitive information such as amounts always be exposed on the blockchain. Therefore, to address these issues, we propose a novel data structure called Reverse-HashTree for Re-storing transactions in blockchain. In addition, we employ a variant of the Paillier public-key cryptosystem to ensure transaction validity without decryption, thus preserving privacy. Building upon these innovations, we designed BillChain, an EB system that enhances supply chain finance by providing privacy-preserving and splitting-enabled E-Bills on the blockchain. This work offers a comprehensive and innovative solution to the challenges faced by E-Bills applied in blockchain in the context of supply chain finance

CCFL: Computationally Customized Federated Learning

Federated learning (FL) is a method to train model with distributed data from numerous participants such as IoT devices. It inherently assumes a uniform capacity among participants. However, participants have diverse computational resources in practice due to different conditions such as different energy budgets or executing parallel unrelated tasks. It is necessary to reduce the computation overhead for participants with inefficient computational resources, otherwise they would be unable to finish the full training process. To address the computation heterogeneity, in this paper we propose a strategy for estimating local models without computationally intensive iterations. Based on it, we propose Computationally Customized Federated Learning (CCFL), which allows each participant to determine whether to perform conventional local training or model estimation in each round based on its current computational resources. Both theoretical analysis and exhaustive experiments indicate that CCFL has the same convergence rate as FedAvg without resource constraints. Furthermore, CCFL can be viewed of a computation-efficient extension of FedAvg that retains model performance while considerably reducing computation overhead