Results 41 to 50 of about 9,950,038 (297)
Peer-to-Peer Learning+Consensus with Non-IID Data
2023 57th Asilomar Conference on Signals, Systems, and Computers, 2023 Peer-to-peer deep learning algorithms are enabling distributed edge devices to collaboratively train deep neural networks without exchanging raw training data or relying on a central server. Peer-to-Peer Learning (P2PL) and other algorithms based on Distributed Local-Update Stochastic/mini-batch Gradient Descent (local DSGD) rely on interleaving epochs
Pranav, Srinivasa, Moura, José M. F.
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Continual Local Training for Better Initialization of Federated Models
, 2020 Federated learning (FL) refers to the learning paradigm that trains machine learning models directly in the decentralized systems consisting of smart edge devices without transmitting the raw data, which avoids the heavy communication costs and privacy ...
Sun, Lifeng, Yao, Xin
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Federated Graph Classification over Non-IID Graphs
, 2021 Federated learning has emerged as an important paradigm for training machine learning models in different domains. For graph-level tasks such as graph classification, graphs can also be regarded as a special type of data samples, which can be collected and stored in separate local systems.
Xie, Han +3 more
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On the Performance of Turbo Signal Recovery with Partial DFT Sensing Matrices [PDF]
, 2015 This letter is on the performance of the turbo signal recovery (TSR) algorithm for partial discrete Fourier transform (DFT) matrices based compressed sensing.
Ma, Junjie, Ping, Li, Yuan, Xiaojun
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Unique Distributions Under Non-IID Assumption
, 2021 Applications of Strongly Convergent M-Estimators are discussed. Given the ubiquity of distributions across the sciences, multiple applications in the Physical, Biomedical and Social Sciences are elaborated. In one particular implementation unique utilities are attained.
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A Privacy-Preserving Collaborative Federated Learning Framework for Detecting Retinal Diseases
IEEE AccessThe rapid advancement in technology has simplified human life and provides convenience. However, this convenience has led to many lifestyle diseases like diabetes and obesity.
Seema Gulati +4 more
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Memory attacks in network nonlocality and self-testing [PDF]
QuantumWe study what can or cannot be certified in communication scenarios where the assumption of independence and identical distribution (iid) between experimental rounds fails.
Mirjam Weilenmann +2 more
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The Poisson transform for unnormalised statistical models
, 2014 Contrary to standard statistical models, unnormalised statistical models only specify the likelihood function up to a constant. While such models are natural and popular, the lack of normalisation makes inference much more difficult.
Barthelmé, Simon, Chopin, Nicolas
core +4 more sources
Iterative Bayesian Reconstruction of Non-IID Block-Sparse Signals [PDF]
IEEE Transactions on Signal Processing, 2016 This paper presents a novel Block Iterative Bayesian Algorithm (Block-IBA) for reconstructing block-sparse signals with unknown block structures. Unlike the existing algorithms for block sparse signal recovery which assume the cluster structure of the nonzero elements of the unknown signal to be independent and identically distributed (i.i.d.), we use ...
Mehdi Korki +3 more
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A Collaborative Privacy Preserved Federated Learning Framework for Pneumonia Detection using Diverse Chest X-ray Data Silos [PDF]
International Journal of Mathematical, Engineering and Management SciencesPneumonia detection from chest X-rays remains one of the most challenging tasks in the traditional centralized framework due to the requirement of data consolidation at the central location raising data privacy and security concerns.
Shagun Sharma, Kalpna Guleria
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