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20242026
most citedNon-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

3 citations · 3 across the 6 of their papers we have counts for

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cs.CR2026

Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers

Daniel M. Jimenez-Gutierrez, Dario Pighin, Enrique Zuazua +4

Federated Learning (FL) enables collaborative model training among multiple parties without centralizing raw data. There are two main paradigms in FL: Horizontal FL (HFL), where al…

cs.DC2026

FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data

Daniel M. Jimenez-Gutierrez, Giovanni Giunta, Mehrdad Hassanzadeh +3

Federated Learning (FL) enables distributed Artificial Intelligence (AI) across cloud-edge environments by allowing collaborative model training without centralizing data. In cross…