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20202026
most citedFedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Quality

22 citations · 87 across the 22 of their papers we have counts for

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9 papers · 1 filter

cs.DC2026

OrbitBFT: Enabling Scalable and Robust BFT Consensus in LEO Constellations

Tianyi Sun, Shuo Liu, Minghui Xu +1

Low Earth Orbit (LEO) satellite constellations are evolving from communication relays into autonomous platforms operating in increasingly congested and contested environments. Sinc…

cs.DC2025

Asynchronous BFT Consensus Made Wireless

Shuo Liu, Minghui Xu, Tianyi Sun +1

Asynchronous Byzantine fault-tolerant (BFT) consensus protocols, known for their robustness in unpredictable environments without relying on timing assumptions, are becoming increa…

cs.DC2024

A Treatment of EIP-1559: Enhancing Transaction Fee Mechanism through Nth-Price Auction

Kun Li, Guangpeng Qi, Guangyong Shang +3

With the widespread adoption of blockchain technology, the transaction fee mechanism (TFM) in blockchain systems has become a prominent research topic. An ideal TFM should satisfy…

cs.DC202422 cited

FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Quality

Biwei Yan, Hongliang Zhang, Minghui Xu +2

Federated learning is a powerful technique that enables collaborative learning among different clients. Prototype-based federated learning is a specific approach that improves the…

cs.DC20231 cited

Spatial Crowdsourcing Task Allocation Scheme for Massive Data with Spatial Heterogeneity

Kun Li, Shengling Wang, Hongwei Shi +2

Spatial crowdsourcing (SC) engages large worker pools for location-based tasks, attracting growing research interest. However, prior SC task allocation approaches exhibit limitatio…

cs.DC2023

PoFEL: Energy-efficient Consensus for Blockchain-based Hierarchical Federated Learning

Shengyang Li, Qin Hu, Zhilin Wang

Facilitated by mobile edge computing, client-edge-cloud hierarchical federated learning (HFL) enables communication-efficient model training in a widespread area but also incurs ad…