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   Articles with "iid data" as a keyword



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

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recommendations! Published in 2024 at "IEEE Access"

DOI: 10.1109/access.2024.3453068

Abstract: 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… read more here.

Keywords: learning; non iid; sentiment analysis; iid data ... See more keywords

Secure Federated Learning for Parkinson’s Disease: Non-IID Data Partitioning and Homomorphic Encryption Strategies

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recommendations! Published in 2024 at "IEEE Access"

DOI: 10.1109/access.2024.3454690

Abstract: In this paper, we explore methods to enhance both the performance and privacy of federated learning models by implementing two key techniques: homomorphic encryption and attention-based fusion. Federated learning which involves client-side training of models… read more here.

Keywords: secure federated; non iid; encryption; federated learning ... See more keywords

Resource-Efficient Federated Learning With Non-IID Data: An Auction Theoretic Approach

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recommendations! Published in 2022 at "IEEE Internet of Things Journal"

DOI: 10.1109/jiot.2022.3197317

Abstract: Federated learning (FL) has gained significant importance for intelligent applications, following data produced on a massive scale by numerous distributed IoT devices. From an FL perspective, the key aspect is that this data is not… read more here.

Keywords: resource efficient; non iid; iid data; auction ... See more keywords

Clustered Federated Multitask Learning on Non-IID Data With Enhanced Privacy

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recommendations! Published in 2023 at "IEEE Internet of Things Journal"

DOI: 10.1109/jiot.2022.3228893

Abstract: Federated learning is a machine learning prgadigm that enables the collaborative learning among clients while keeping the privacy of clients’ data. Federated multitask learning (FMTL) deals with the statistic challenge of non-independent and identically distributed… read more here.

Keywords: non iid; multitask learning; iid data; privacy ... See more keywords

Quantized FedPD (QFedPD): Beyond Conventional Wisdom—The Energy Benefits of Frequent Communication

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recommendations! Published in 2025 at "IEEE Internet of Things Journal"

DOI: 10.1109/jiot.2024.3464239

Abstract: Federated averaging (FedAvg) is a well-recognized framework for distributed learning that efficiently manages communication. Several algorithms have emerged to enhance the communication efficiency of FedAvg and its variations. Some of these algorithms focus on reducing… read more here.

Keywords: energy; quantized fedpd; non iid; communication ... See more keywords

HeteroSFL: Split Federated Learning With Heterogeneous Clients and Non-IID Data

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recommendations! Published in 2025 at "IEEE Internet of Things Journal"

DOI: 10.1109/jiot.2025.3572393

Abstract: Split Federated Learning (SFL) is an emerging privacy-preserving decentralized learning scheme which splits a machine learning model between client and server such that most of the computations are offloaded to the server. While SFL has… read more here.

Keywords: split federated; heterogeneous clients; non iid; federated learning ... See more keywords

Asynchronous Federated Learning-Based Indoor Localization With Non-IID Data

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recommendations! Published in 2024 at "IEEE Sensors Journal"

DOI: 10.1109/jsen.2024.3457780

Abstract: In fingerprint-based indoor localization, the collection of clients’ location data may cause serious privacy concerns. The integration of federated learning (FL) into fingerprint-based indoor localization facilitates privacy-preserving distributed training. However, the non-independently and identically distributed… read more here.

Keywords: indoor localization; non iid; localization; based indoor ... See more keywords
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Adaptive Federated Learning on Non-IID Data with Resource Constraint

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recommendations! Published in 2021 at "IEEE Transactions on Computers"

DOI: 10.1109/tc.2021.3099723

Abstract: Federated learning (FL) has been widely recognized as a promising approach by enabling individual end-devices to cooperatively train a global model without exposing their own data. One of the key challenges in FL is the… read more here.

Keywords: non iid; federated learning; iid data; model ... See more keywords

FEEL: Federated End-to-End Learning With Non-IID Data for Vehicular Ad Hoc Networks

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recommendations! Published in 2022 at "IEEE Transactions on Intelligent Transportation Systems"

DOI: 10.1109/tits.2022.3190294

Abstract: Recent studies have demonstrated the potentials of federated learning (FL) in achieving cooperative and privacy-preserving data analytics. It would also be promising if FL can be employed in vehicular ad hoc networks (VANETs) for cooperative… read more here.

Keywords: non iid; federated end; hoc networks; iid data ... See more keywords

Federated Learning with Taskonomy for Non-IID Data

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recommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"

DOI: 10.1109/tnnls.2022.3152581

Abstract: Classical federated learning approaches incur significant performance degradation in the presence of non-independent and identically distributed (non-IID) client data. A possible direction to address this issue is forming clusters of clients with roughly IID data.… read more here.

Keywords: non iid; federated learning; learning taskonomy; taskonomy non ... See more keywords

Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data

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recommendations! Published in 2022 at "IEEE Transactions on Parallel and Distributed Systems"

DOI: 10.1109/tpds.2022.3150579

Abstract: Federated learning (FL) is an emerging privacy-preserving paradigm that enables multiple participants collaboratively to train a global model without uploading raw data. Considering heterogeneous computing and communication capabilities of different participants, asynchronous FL can avoid… read more here.

Keywords: non iid; iid data; federated learning; stale gradients ... See more keywords