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Structured and sketched updates for communication-efficient federated learning

Contribution grade

Federated update compression · Human-designed research method or resource

Reference score105,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Evaluated low-rank/random-mask structured updates and post-update subsampling/probabilistic quantization, using structured random rotations to improve compressed update quality.

Scientific scope

Evaluated low-rank/random-mask structured updates and post-update subsampling/probabilistic quantization, using structured random rotations to improve compressed update quality. A communication layer distinct from FedAvg's local-step aggregation. Unbiased estimators under specified sampling do not imply identical learning trajectories or universal convergence; high-rank compression degraded the tested convolutions. CIFAR-10 upload-budget gains are not arbitrary-task accuracy guarantees or privacy protection.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. A communication layer distinct from FedAvg's local-step aggregation. Unbiased estimators under specified sampling do not imply identical learning trajectories or universal convergence; high-rank compression degraded the tested convolutions. CIFAR-10 upload-budget gains are not arbitrary-task accuracy guarantees or privacy protection.

Contributors

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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