On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,694 papers · 148 categories

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4 results for “AirComp”

Differentially Private Wireless Federated Learning Using Orthogonal Sequencescs.IT

FLORAS uses orthogonal sequences for SISO FL, offering both DP and convergence guarantees.

problem Privacy-preserving wireless federated learning in SISO systems.

method Leverages orthogonal sequences to eliminate CSIT requirement and provide DP guarantees.

result FLORAS achieves a smooth tradeoff between convergence rate and DP levels.

differential privacy federated learning orthogonal sequences wireless communication

2023-06-14abs ↗pdf ↗

Federated Linear Bandit Learning via Over-the-Air Computationcs.LG

Paper tackles federated linear bandit learning with AirComp for noisy channels.

problem Minimize cumulative regret in federated linear bandit learning.

method Proposes a federated linear bandits scheme using over-the-air computation (AirComp) over noisy fading channels.

result Determines the regret bound of the proposed scheme.

federated learning linear bandits over-the-air computation noisy channels regret analysis

2023-08-25abs ↗pdf ↗

Blind Over-the-Air Computation and Data Fusion via Provable Wirtinger Flowcs.IT

Over-the-air computation (AirComp) shows great promise to support fast data fusion in Internet-of-Things (IoT) networks. AirComp typically computes desired functions of distributed sensing data by exploiting superposed data transmission in multiple access channels. To overcome its reliance on channel station informatio…

2018-11-12abs ↗pdf ↗

Differential Privacy as a Perk: Federated Learning over Multiple-Access Fading Channels with a Multi-Antenna Base Stationcs.LG

This paper shows how differential privacy can be achieved naturally in federated learning over fading channels without artificial noise.

problem Achieving differential privacy in federated learning over fading channels without artificial noise.

method Study of AirFL over multiple-access fading channels with a multi-antenna base station, deriving novel bounds on differential privacy.

result DP can be achieved naturally in federated learning over fading channels without artificial noise, revealing convergence-privacy trade-offs.

Federated Learning Differential Privacy Fading Channels AirComp Multi-Antenna Base Station

2025-10-27abs ↗pdf ↗