ARXIV:2603.10763 · FEDERATED LEARNING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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Prioritizing Gradient Sign Over Modulus: An Importance-Aware Framework for Wireless Federated Learning

arXiv

A framework that enhances wireless federated learning by prioritizing important gradient information for efficient resource allocation.

Blocked on Code›Score4.0Evidence unverified

Opportunity summary

Pain A framework that enhances wireless federated learning by prioritizing important gradient information for efficient resource allocation.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

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PROBLEM

A framework that enhances wireless federated learning by prioritizing important gradient information for efficient resource allocation. However, the inherent constraints of limited wireless resources inevitably lead to unreliable communication, which poses a significant challenge…

METHOD

Full abstract

Wireless federated learning (FL) facilitates collaborative training of artificial intelligence (AI) models to support ubiquitous intelligent applications at the wireless edge. However, the inherent constraints of limited wireless resources inevitably lead to unreliable communication, which poses a significant challenge to wireless FL. To overcome this challenge, we propose Sign-Prioritized FL (SP-FL), a novel framework that improves wireless FL by prioritizing the transmission of important gradient information through uneven resource allocation. Specifically, recognizing the importance of descent direction in model updating, we transmit gradient signs in individual packets and allow their reuse for gradient descent if the remaining gradient modulus cannot be correctly recovered. To further improve the reliability of transmission of important information, we formulate a hierarchical resource allocation problem based on the importance disparity at both the packet and device levels, optimizing bandwidth allocation across multiple devices and power allocation between sign and modulus packets. To make the problem tractable, the one-step convergence behavior of SP-FL, which characterizes data importance at both levels in an explicit form, is analyzed. We then propose an alternating optimization algorithm to solve this problem using the Newton-Raphson method and successive convex approximation (SCA). Simulation results confirm the superiority of SP-FL, especially in resource-constrained scenarios, demonstrating up to 9.96\% higher testing accuracy on the CIFAR-10 dataset compared to existing methods.

RESULT

ScienceToStartup currently rates this 4.0/10 on the public viability pass. Wireless federated learning (FL) facilitates collaborative training of artificial intelligence (AI) models to support ubiquitous intelligent applications at the wireless edge.

WHY NOW

Federated Learning moved forward this cycle; last verified April 2026. Public score 4.0/10.

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Opportunity summary

Score4.0

PainA framework that enhances wireless federated learning by prioritizing important gradient information for efficient resource allocation.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A framework that enhances wireless federated learning by prioritizing important gradient information for efficient resource allocation.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Competitive landscape

A framework that enhances wireless federated learning by prioritizing important gradient information for efficient resource allocation.

Segment

Federated Learning

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No public code link in the paper record yet

Commercial read

4.0/10 public viability

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Competitive landscape

A framework that enhances wireless federated learning by prioritizing important gradient information for efficient resource allocation.

Segment

Federated Learning

Adoption evidence

No public code link in the paper record yet

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4.0/10 public viability

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not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Prioritizing Gradient Sign Over Modulus: An Importance-Aware Framework for Wireless Federated Learning

Prioritizing Gradient Sign Over Modulus: An Importance-Aware Framework for Wireless Federated Learning

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