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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.

168,657 papers · 148 categories

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5 results for “Push-Sum”

Stability and Generalization of Push-Sum Based Decentralized Optimization over Directed Graphscs.LG

Unified framework for stability and generalization of Push-Sum in decentralized learning over directed graphs.

problem Understanding stability and generalization of Push-Sum in decentralized learning over directed networks.

method Developed a unified uniform-stability framework for SGP algorithm, incorporating imbalance-aware consistency bounds.

result Established finite-iteration stability and optimization guarantees for convex and non-convex objectives.

Push-Sum Decentralized Learning Directed Graphs Stability Generalization

2026-02-24abs ↗pdf ↗

Decentralized Hyper-Gradient Computation over Time-Varying Directed Networksstat.ML

Paper tackles hyper-gradient estimation in decentralized FL over time-varying networks.

problem Excessive communication costs and inability to use robust networks.

method Introduces an optimality condition and uses Push-Sum for averaging model parameters and gradients over time-varying directed networks.

result Derives a hyper-gradient estimator that operates over time-varying directed networks and converges to the true hyper-gradient.

Decentralized Federated Learning Hyper-Gradient Estimation Push-Sum Time-Varying Networks Influence Estimation

2022-10-05abs ↗pdf ↗

Push-SAGA: A decentralized stochastic algorithm with variance reduction over directed graphscs.LG

Push-SAGA is a decentralized algorithm for directed graphs that converges linearly.

problem Finite-sum minimization over directed graphs with stochastic gradients.

method Combines variance reduction, gradient tracking, and consensus algorithms.

result Achieves linear convergence for smooth and strongly convex problems.

Decentralized optimization Directed graphs Variance reduction Gradient tracking Push-sum

2020-08-13abs ↗pdf ↗

Central Server Free Federated Learning over Single-sided Trust Social Networkscs.LG

Federated learning has become increasingly important for modern machine learning, especially for data privacy-sensitive scenarios. Existing federated learning mostly adopts the central server-based architecture or centralized architecture. However, in many social network scenarios, centralized federated learning is not…

2019-10-11abs ↗pdf ↗

Variance reduced stochastic optimization over directed graphs with row and column stochastic weightsmath.OC

AB-SAGA optimizes distributed optimization over directed graphs using variance reduction and stochastic weights.

problem Optimizing distributed stochastic optimization over directed graphs with stochastic weights.

method AB-SAGA combines variance reduction and network-level gradient tracking, using both row and column stochastic weights.

result AB-SAGA converges linearly to the global optimal with a constant step-size and achieves a linear speed-up over centralized methods.

distributed optimization stochastic gradient descent variance reduction directed graphs stochastic weights

2022-02-07abs ↗pdf ↗