Results 61 to 70 of about 9,950,038 (297)
Byzantine-robust federated learning via credibility assessment on non-IID data
Mathematical Biosciences and Engineering, 2022 Federated learning is a novel framework that enables resource-constrained edge devices to jointly learn a model, which solves the problem of data protection and data islands.
Kun Zhai +3 more
doaj +1 more source
Incorporating couplings into collaborative filtering [PDF]
, 2016 University of Technology Sydney. Faculty of Engineering and Information Technology.Recommender Systems (RS) have been proposed to help users tackle information overload by suggesting potentially interesting items to users.
Li, Fangfang
core
Advanced Science, EarlyView.Exclusive breastfeeding establishes a thermogenic memory in brown adipose tissue by activating the HIF1AN/AMPK/α‐ketoglutarate axis via milk‐derived extracellular vesicles enriched in miR‐125a‐5p. This programming preserves metabolic health, while αKG supplementation restores BAT function under mixed feeding, offering strategies to mitigate the ...
Ningxi Wu +13 more
wiley +1 more source
Federated PAC-Bayesian Learning on Non-IID Data
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)Existing research has either adapted the Probably Approximately Correct (PAC) Bayesian framework for federated learning (FL) or used information-theoretic PAC-Bayesian bounds while introducing their theorems, but few considering the non-IID challenges in FL. Our work presents the first non-vacuous federated PAC-Bayesian bound tailored for non-IID local
Zhao, Zihao +3 more
openaire +3 more sources
Multi-Level Coupling Network for Non-IID Sequential Recommendation
IEEE Access, 2019 Sequential recommendation has been recently attracting a lot attention to suggest users with next items to interact. However, most of the traditional studies implicitly assume that users and items are independent and identically distributed (IID) and ...
Yatong Sun +3 more
doaj +1 more source
Entropy-Driven Stochastic Federated Learning in Non-IID 6G Edge-RAN
Frontiers in Communications and Networks, 2021 Scalable and sustainable AI-driven analytics are necessary to enable large-scale and heterogeneous service deployment in sixth-generation (6G) ultra-dense networks.
Brahim Aamer +3 more
doaj +1 more source
On the exact moments of non-standard asymptotic distributions in non stationary autoregressions with dependent errors [PDF]
, 1995 In this paper we derive the exact moments of the asymptotic distributions of the OLS estimate and t-statistic in an unstable AR(1) with dependent errors.
Gonzalo, Jesús, Pitarakis, Jean-Yves
core +1 more source
EGR Proteins Mediate Interferon‐Independent Anti‐HSV‐1 Responses Through Viral and Host Targets
Advanced Science, EarlyView.Early antiviral responses are typically mediated by interferons. However, during HSV‐1 infection, host early growth response (Egr) genes, which are not interferon‐stimulated genes, are quickly induced by viral protein ICP0. EGR proteins, in turn, suppress viral lytic infection by activating viral latency‐associated (LAT) and host immune regulatory ...
Shuaishuai Wang +4 more
wiley +1 more source
FedSC: A federated learning algorithm based on client-side clustering
Electronic Research Archive, 2023 In traditional centralized machine learning frameworks, the consolidation of all data in a central data center for processing poses significant concerns related to data privacy breaches and data sharing complexities.
Zhuang Wang +3 more
doaj +1 more source
RRAM Variability Harvesting for CIM‐Integrated TRNG
Advanced Electronic Materials, EarlyView.This work demonstrates a compute‐in‐memory‐compatible true random number generator that harvests intrinsic cycle‐to‐cycle variability from a 1T1R RRAM array. Parallel entropy extraction enables high‐throughput bit generation without dedicated circuits. This approach achieves NIST‐compliant randomness and low per‐bit energy, offering a scalable hardware
Ankit Bende +4 more
wiley +1 more source