AWhat changed
Analyzed how unequal local update counts/solvers change implicit aggregation weights, and normalized accumulated local gradients before averaging to target the intended global objective.
Scientific scope
Analyzed how unequal local update counts/solvers change implicit aggregation weights, and normalized accumulated local gradients before averaging to target the intended global objective. Consistency removes the specified surrogate-objective bias under modeled accumulation/sampling, not every source of non-IID drift, fairness or generalization failure. Rates need smoothness, stochastic-gradient and heterogeneity assumptions and suitable local learning rates. Permitted local solvers have the specified linear accumulation structure; arbitrary optimizers cannot be inserted without checking it.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Consistency removes the specified surrogate-objective bias under modeled accumulation/sampling, not every source of non-IID drift, fairness or generalization failure. Rates need smoothness, stochastic-gradient and heterogeneity assumptions and suitable local learning rates. Permitted local solvers have the specified linear accumulation structure; arbitrary optimizers cannot be inserted without checking it.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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