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ARXIV:2601.20571 · DISTRIBUTED LEARNING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2601.20571DISTRIBUTED LEARNINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Gossip algorithm for efficient and robust decentralized median and quantile estimation on edge devices.
Opportunity summary
Pain Gossip algorithm for efficient and robust decentralized median and quantile estimation on edge devices.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Gossip algorithm for efficient and robust decentralized median and quantile estimation on edge devices. While state-of-the-art gossip-based methods satisfy the first requirement, achieving robustness remains challenging.
Specifications for decentralized learning on resource-constrained edge devices require algorithms that are communication-efficient, robust to data corruption, and lightweight in memory usage. While state-of-the-art gossip-based methods satisfy the first requirement, achieving robustness remains challenging.
ScienceToStartup currently rates this 5.0/10 on the public viability pass. We analyze a synchronous variant of AsylADMM to establish theoretical guarantees and empirically demonstrate fast convergence for the asynchronous algorithm.
Distributed Learning moved forward this cycle; last verified April 2026. Public score 5.0/10.
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Gossip algorithm for efficient and robust decentralized median and quantile estimation on edge devices.
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10.48550/arXiv.2601.20571Gossip algorithm for efficient and robust decentralized median and quantile estimation on edge devices.
Abstract
Specifications for decentralized learning on resource-constrained edge devices require algorithms that are communication-efficient, robust to data corruption, and lightweight in memory usage. While state-of-the-art gossip-based methods satisfy the first requirement, achieving robustness remains challenging. Asynchronous decentralized ADMM-based methods have been explored for estimating the median, a statistical centrality measure that is notoriously more robust than the mean. However, existing approaches require memory that scales with node degree, making them impractical when memory is limited. In this paper, we propose AsylADMM, a novel gossip algorithm for decentralized median and quantile estimation, primarily designed for asynchronous updates and requiring only two variables per node. We analyze a synchronous variant of AsylADMM to establish theoretical guarantees and empirically demonstrate fast convergence for the asynchronous algorithm. We then show that our algorithm enables quantile-based trimming, geometric median estimation, and depth-based trimming, with quantile-based trimming empirically outperforming existing rank-based methods. Finally, we provide a novel theoretical analysis of rank-based trimming via Markov chain theory.
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PROBLEM
Gossip algorithm for efficient and robust decentralized median and quantile estimation on edge devices. While state-of-the-art gossip-based methods satisfy the first requirement, achieving robustness remains challenging.
METHOD
Specifications for decentralized learning on resource-constrained edge devices require algorithms that are communication-efficient, robust to data corruption, and lightweight in memory usage. While state-of-the-art gossip-based methods satisfy the first requirement, achieving ro...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. We analyze a synchronous variant of AsylADMM to establish theoretical guarantees and empirically demonstrate fast convergence for the asynchronous algorithm.
WHY NOW
Distributed Learning moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Gossip algorithm for efficient and robust decentralized median and quantile estimation on edge devices. While state-of-the-art gossip-based methods satisfy the first requirement, achieving robustness remains challenging.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Specifications for decentralized learning on resource-constrained edge devices require algorithms that are communication-efficient, robust to data corruption, and lightweight in memory usage. While state-of-the-art gossip-based methods satisfy the first requirement, achieving robustness remains challenging.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. We analyze a synchronous variant of AsylADMM to establish theoretical guarantees and empirically demonstrate fast convergence for the asynchronous algorithm.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Distributed Learning moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Gossip algorithm for efficient and robust decentralized median and quantile estimation on edge devices.
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