{"data":{"id":"7bd01385-9dac-4a58-869a-413725e96ade","title":"Asynchronous Federated Learning With Nonconvex Client Objective Functions and Heterogeneous Dataset","summary":"This article extends asynchronous federated learning (AFL) to clients with nonconvex objective functions and heterogeneous datasets. The authors provide a convergence analysis that bounds the expected gradient norm and examines the effects of staleness, variance and heterogeneity. They introduce a staleness-aware aggregation mechanism and a dynamic learning rate schedule, and implement the methods in PyTorch with Python's asyncio library.","solution":"N/A -- no mitigation discussed in source.","labels":["research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11202971","publishedAt":"2025-10-14T13:16:41.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":[],"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":"2025-10-14T13:16:41.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":null,"aiComponentTargeted":"framework","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}