Results 31 to 40 of about 14,306 (259)

Federated Conditional Variational Auto Encoders for Cyber Threat Intelligence: Tackling Non-IID Data in SDN Environments

IEEE Access
Federated Learning is a promising paradigm for sharing Cyber Threat Intelligence (CTI) without privacy issues by leveraging the cross-silos data in Software Defined Networking (SDN).
Syed Hussain Ali Kazmi, Rosilah Hassan, Faizan Qamar, Kashif Nisar, Mohammed Azmi Al-Betar   +4 more
doaj   +1 more source

Memory attacks in network nonlocality and self-testing [PDF]

Quantum
We 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, Costantino Budroni, Miguel Navascues   +2 more
doaj   +1 more source

Balancing Privacy and Performance: A Differential Privacy Approach in Federated Learning

Computers
Federated learning (FL), a decentralized approach to machine learning, facilitates model training across multiple devices, ensuring data privacy. However, achieving a delicate privacy preservation–model convergence balance remains a major problem ...
Huda Kadhim Tayyeh, Ahmed Sabah Ahmed AL-Jumaili   +1 more
doaj   +1 more source

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 Sciences
Pneumonia 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
doaj   +1 more source

Global Layers: Non-IID Tabular Federated Learning

CoRR, 2023
Data heterogeneity between clients remains a key challenge in Federated Learning (FL), particularly in the case of tabular data. This work presents Global Layers (GL), a novel partial model personalization method robust in the presence of joint distribution $P(X,Y)$ shift and mixed input/output spaces $X \times Y$ across clients.
openaire   +2 more sources

Continual Learning for Multimodal Data Fusion of a Soft Gripper

Advanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley   +1 more source

The KIF6‐RBP Complex Orchestrates mRNA Transport Required for Sperm Flagellar Assembly

Advanced Science, EarlyView.
Two homozygous deleterious KIF6 variants are identified in unrelated men with impaired sperm motility. Mouse models and multi‐omics analyses reveal that KIF6 cooperates with the RNA‐binding proteins FMRP and FXR1 to deliver mRNAs essential for sperm flagellar assembly, linking disrupted mRNA transport to reduced abundance of key structural and ...
Chunbo Xie, Sibing Yi, Xinle Lin, Shen Zhang, Weili Wang, Shimin Yuan, Lanlan Meng, Yong Li, Chen Tan, Chunjia Wei, Yanyan Yu, Yaoqiong Liang, Huan Zhang, Liang Hu, Guangxiu Lu, Wenbin He, Qianjun Zhang, Juan Du, Ge Lin, Chaofeng Tu, Yue‐Qiu Tan   +20 more
wiley   +1 more source

StackingNet: Collective Inference Across Independent AI Foundation Models

Advanced Science, EarlyView.
ABSTRACT Artificial intelligence (AI) built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readily share their capabilities. Coordinating the complementary strengths of independently developed, black‐box foundation models is essential for trustworthy ...
Siyang Li, Chenhao Liu, Dongrui Wu, Zhigang Zeng, Lieyun Ding   +4 more
wiley   +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, Gokulnath Rajendran, Regina Dittmann, Anupam Chattopadhyay, Vikas Rana   +4 more
wiley   +1 more source

Explainable Federated Learning for Brain Tumor Classification Using Multi-Source MRI Data

Iraqi Journal for Computers and Informatics
Early diagnosis and clinical decision-making depend on accurate brain tumor classification using magnetic resonance imaging (MRI). However, traditional deep learning methods usually rely on centralized medical data, which raises privacy concerns and ...
Suhad Muhy Helal, Belal Al-Khateeb
doaj   +1 more source