Giskard is a new protocol using tree-structured log-sized committees and MPC-based approximate median to achieve scalable confidential and Byzantine-robust aggregation in decentralized learning.
North American Actuarial Journal , volume =
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Joint exclusivity extends mutual exclusivity with a sharp existence condition sum of marginal survival functions at zero at most n-1, a canonical construction on lower-dimensional faces, and a link to joint mixability.
NetNomos is a multi-stage framework that extracts, filters, and enforces first-order logic rules in generative ML models for networking tasks including telemetry imputation, traffic forecasting, and synthetic trace generation.
First empirical test finds TabPFN fails to beat GLM or XGBoost in insurance pricing while incurring higher inference cost and sensitivity to context size.
Convexification enables reliable solutions of Mean Field Games whose predicted sentiment densities align with observed COVID-19 social media discussion patterns.
The paper introduces a penalized distributed lag non-linear Lee-Carter framework that adds temperature and influenza effects, negative binomial overdispersion, SARIMA dynamics, and copula dependence for improved regional weekly mortality forecasts on French data 1990-2019.
An in-vehicle digital twin framework using temporal convolutional networks and hierarchical navigable small world algorithms detects Sybil attacks with 0.984 accuracy and reduces near-collision metrics by 72-88% on real-world field data.
CNN-LSTM and GNN-LSTM models added to a Lee-Carter baseline reduce test MSE by about 24% versus MortFCNet on French regional mortality data from 1990-2019, with largest gains at oldest ages.
Neighbor-only work stealing for 2D-mesh satellite constellations yields growing per-attempt latency advantages and performs within 2.2% of global stealing on emulated workloads.
Family-FL uses family-level aggregation in a three-tier setup with a sub-5KB quantized CNN-LSTM to cut communication by 76.7% versus FedAvg while reaching 91.9% accuracy on MIT-BIH arrhythmia data.
Survey classifying 78 joint OFDM-RIS optimization papers into convex relaxation, heuristics, deep learning, and foundation model paradigms, with synthesis showing ML methods achieve near model-based spectral efficiency at much higher speed.
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Optimization Algorithms for Joint OFDM Waveform Design and RIS Configuration in 6G Networks: From Convex Relaxation to Foundation Models
Survey classifying 78 joint OFDM-RIS optimization papers into convex relaxation, heuristics, deep learning, and foundation model paradigms, with synthesis showing ML methods achieve near model-based spectral efficiency at much higher speed.