Results 61 to 70 of about 34,864,804 (179)

Federated two-stage transformer-based network for intrusion detection in non-IID data of controller area networks

Cybersecurity
The modern vehicle employs the Controller Area Network (CAN) to facilitate communication among its electronic control units (ECUs). However, the absence of encryption and authentication renders the CAN vulnerable to various attacks.
Yuan Zhang, Jiaru Song, Yongxiong Sun, Zhan Gao, Zhe Hu, Minghui Sun   +5 more
semanticscholar   +1 more source

Federated Learning for Sentiment Analysis in Presence of Non-IID Data: Sensitivity of Deep Learning Models

IEEE Access
In sentiment analysis, data are commonly distributed across many devices, and traditional machine learning requires transferring these data to a central location exposing data to security and privacy risks. Federated Learning (FL) avoids this transfer by
Davoud Gholamiangonabadi, Katarina Grolinger   +1 more
doaj   +1 more source

Overcoming Forgetting in Federated Learning on Non-IID Data

CoRR, 2019
We tackle the problem of Federated Learning in the non i.i.d. case, in which local models drift apart, inhibiting learning. Building on an analogy with Lifelong Learning, we adapt a solution for catastrophic forgetting to Federated Learning. We add a penalty term to the loss function, compelling all local models to converge to a shared optimum. We show
Neta Shoham, Tomer Avidor, Aviv Keren, Nadav Israel, Daniel Benditkis, Liron Mor-Yosef, Itai Zeitak   +6 more
openaire   +2 more sources

Privacy-preserved learning from non-iid data in fog-assisted IoT: A federated learning approach

, 2022
Privacy-preserved learning from non-iid data in fog-assisted IoT: A federated learning ...
Mohamed Abdel-Basset (5152634), Hossam Hawash (18173107), Nour Moustafa (9644231), Mohamed Abd Elfattah (18173110), Imran Razzak (13074405)   +4 more
core  

Handling Non-IID Data in Federated Learning : An Experimental Evaluation Towards Unified Metrics

, 2023
Recent research has demonstrated that Non-Identically Distributed (Non-IID) data can negatively impact the performance of global models constructed in federated learning. To address this concern, multiple approaches have been developed.
Haller, Marc,, Lenz, Christian, Lenz, Christian,, Nachtigall, Robin,, Awayshehl, Feras M.,, Awayshehl, Feras M., Alawadi, Sadi,, Haller, Marc, Alawadi, Sadi, Nachtigall, Robin   +9 more
core   +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

Non-IID scenario: 10-fold cross validation results with varying C.

, 2020
Non-IID scenario: 10-fold cross validation results with varying C.
Zeng Fu (8727135), Dianbo Liu (8311149), Li Huang (107408), Yifeng Yin (2992161), Hao Deng (409186), Shifa Zhang (8727138)   +5 more
core   +1 more source

Adding Data Quality to Federated Learning Performance Improvement

IEEE Access
Massive data generation from Internet of Things (IoT) devices increases the demand for efficient data analysis to extract relevant and actionable insights. As a result, Federated Learning (FL) allows IoT devices to collaborate in Artificial Intelligence (
Ernesto Gurgel Valente Neto, Solon Alves Peixoto, Valderi Reis Quietinho Leithardt, Juan Francisco de Paz Santana, Julio C. S. Dos Anjos   +4 more
doaj   +1 more source

Mitigating Backdoor Attacks in Federated Learning Systems Under Non‑IID Data: A Comprehensive Survey

IJCI International Journal of Computers and Information
Federated Learning (FL) enables collaborative model training while preserving data privacy, but its decentralized nature exposes it to backdoor attacks, where malicious clients inject poisoned updates that embed hidden triggers into the global model ...
Ahmed Soliman, Khalid Amin, Noura Semary, Hayam Mousa   +3 more
doaj   +1 more source

FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated Learning [PDF]

Logical Methods in Computer Science
In the last years, Federated learning (FL) has become a popular solution to train machine learning models in domains with high privacy concerns. However, FL scalability and performance face significant challenges in real-world deployments where data ...
Davide Domini, Gianluca Aguzzi, Lukas Esterle, Mirko Viroli   +3 more
doaj   +1 more source