{"data":{"id":"3b3820d1-57a2-4fb0-a664-ac97239c58f5","title":"FedReason: Large Language Models as Weight-Space Meta-Aggregators for Heterogeneous Federated Learning","summary":"FedReason uses a large language model to choose the server-side aggregation rule in federated learning, calling it every ten rounds to analyse client weights in a restricted sandbox and submit a short Python aggregation program, falling back to FedAvg when output is invalid. Evaluated with Claude Opus 5.5 on CIFAR-10 and CIFAR-100 with ResNet-18 across 168 runs, it beat FedAvg by 1.26 percentage points on average across 12 paired label-skew runs. It remained about one point below FLTrust under Byzantine clients, and accuracy fell from 73.8% to 38.8% in one seed without proxy feedback.","solution":"N/A -- no mitigation discussed in source.","labels":["research"],"sourceUrl":"https://arxiv.org/abs/2611.08383v1","publishedAt":"2026-11-05T18:00:58.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":["Anthropic"],"affectedVendorsRaw":["Claude Opus 5.5"],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","headline":null,"headlinePromptVersion":null,"cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"epssCheckedAt":null,"kevDateAdded":null,"advisoryAliases":null,"affectedPackagesSource":null,"affectedPackagesCheckedAt":null,"patchAvailable":null,"disclosureDate":"2026-11-05T18:00:58.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":null,"aiComponentTargeted":"framework","llmSpecific":true,"classifierConfidence":0.85,"researchCategory":"preprint","atlasIds":null}}