pith:RQKZDCSK
Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning
REED recovers signed model updates from energy differences on two orthogonal resources without needing instantaneous channel state information.
arxiv:2605.07263 v2 · 2026-05-08 · eess.SP · cs.AI · cs.DC · cs.LG · stat.ML
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Claims
REED is unbiased for the desired signed sum and admits an exact closed-form variance under Rayleigh fading. We incorporate REED into full-participation FedAvg and prove a smooth nonconvex stationarity bound... yielding the canonical (1/sqrt(T)) stationarity rate.
The analysis and experiments rely on Rayleigh fading, slow-timescale average channel power calibration, an average per-client energy budget, and either IID or moderate heterogeneity on MNIST/Fashion-MNIST; the stationarity bound assumes the aggregation gain can be scheduled to make the REED perturbation scale quadratically with the local stepsize.
REED enables unbiased noncoherent signed aggregation in OTA-FL via energy differences on orthogonal resources, achieving closed-form variance under Rayleigh fading and standard (1/sqrt(T)) convergence in FedAvg.
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Receipt and verification
| First computed | 2026-05-20T00:05:46.456144Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
8c15918a4a29d2119cf64dc6a52c8ddbe1786f3a84b256e6eda5f0ed3149a519
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/RQKZDCSKFHJBDHHWJXDKKLEN3P \
| jq -c '.canonical_record' \
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Canonical record JSON
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