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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2605.04091 |
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| _version_ | 1866917462437527552 |
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| author | Jia, Wenyang Xu, Qiankang Yan, Ziwei Kang, Chunhua Yang, Yang He, Jinglu Lei, Kai |
| author_facet | Jia, Wenyang Xu, Qiankang Yan, Ziwei Kang, Chunhua Yang, Yang He, Jinglu Lei, Kai |
| contents | Decentralized Federated Learning (DFL) eliminates the central aggregator but introduces a severe 'trust gap': without a trusted coordinator, the system becomes vulnerable to Byzantine and Sybil attacks, while existing solutions treat node selection, aggregation, and consensus as isolated modules, often relying on a trusted root dataset unavailable in truly decentralized settings.We propose OpenCLAW-Nexus, a self-reinforcing trust framework that bridges this gap through a single primitive, a discounted Beta-reputation model, that unifies reputation-based node selection, reputation-weighted aggregation Rep-FedAvg, and reputation-aware BFT consensus. Rep-FedAvg eliminates the trusted root dataset requirement; we formally prove reputation separation between honest and Byzantine nodes under non-IID data with noisy evaluations.On a 1,000-node global testbed spanning three cloud providers and nine regions, Rep-FedAvg achieves 72.6% accuracy on non-IID CIFAR-10 with 20% Byzantine nodes and record-level differential privacy, within 0.5,pp of centralized FLTrust.Under a 300-node Sybil attack, reputation-weighted consensus maintains 84.2% validation correctness versus 62.8% (PoW) and 47.6% (PoS). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_04091 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OpenCLAW-Nexus: A Self-Reinforcing Trust Framework for Byzantine-Resilient Decentralized Federated Learning Jia, Wenyang Xu, Qiankang Yan, Ziwei Kang, Chunhua Yang, Yang He, Jinglu Lei, Kai Networking and Internet Architecture Decentralized Federated Learning (DFL) eliminates the central aggregator but introduces a severe 'trust gap': without a trusted coordinator, the system becomes vulnerable to Byzantine and Sybil attacks, while existing solutions treat node selection, aggregation, and consensus as isolated modules, often relying on a trusted root dataset unavailable in truly decentralized settings.We propose OpenCLAW-Nexus, a self-reinforcing trust framework that bridges this gap through a single primitive, a discounted Beta-reputation model, that unifies reputation-based node selection, reputation-weighted aggregation Rep-FedAvg, and reputation-aware BFT consensus. Rep-FedAvg eliminates the trusted root dataset requirement; we formally prove reputation separation between honest and Byzantine nodes under non-IID data with noisy evaluations.On a 1,000-node global testbed spanning three cloud providers and nine regions, Rep-FedAvg achieves 72.6% accuracy on non-IID CIFAR-10 with 20% Byzantine nodes and record-level differential privacy, within 0.5,pp of centralized FLTrust.Under a 300-node Sybil attack, reputation-weighted consensus maintains 84.2% validation correctness versus 62.8% (PoW) and 47.6% (PoS). |
| title | OpenCLAW-Nexus: A Self-Reinforcing Trust Framework for Byzantine-Resilient Decentralized Federated Learning |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2605.04091 |