2,250 research outputs found
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
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