Refined Differentially Private Linear Regression via Extension of a Free Lunch Result
2026-04-15T08:51:42Z•d4001f0fc7fef23458902b6023cf444e35ee4c86d8077a648e5c4d5cf69ac30e
Gaussian-belief-propagationLDPCPMUTurán-theoryUAVcoding-theorydecentralized-algorithmsdifferential-privacydiffusion-modelsemergency-communicationsfederated-learninglinear-regressionmulti-agent-RLpath-compensationpath-planningpower-systemspreemptive-schedulingsecure-aggregationsecure-gradient-coding','groupwise-keyssemantic-communicationsimplex-transformationstate-estimationsuccessive-interference-cancellationtrapping-setswireless-scheduling
What happened
Collection of new arXiv submissions (Apr 15 2026) across privacy-preserving ML, communications, coding, control, and power-system estimation. Key contributions include: (1) an extension of the “free lunch” simplex transformation for add/remove differential privacy to refine sufficient-statistic estimates for private ordinary least-squares linear regression (and polynomial regression); (2) CAPS-TDPC — a channel-aware preemptive scheduling framework that interrupts truncated diffusion-based semantic generation for early wireless transmission with receiver-side path-compensation; (3) a vectorized
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_math_it
- Record identifier
- d4001f0fc7fef23458902b6023cf444e35ee4c86d8077a648e5c4d5cf69ac30e
- Enrichment time
- 2026-04-15T08:51:42Z
- AI-assisted enrichment
- Yes
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