Home › Entities › academia › Addressing Heterogeneity in Federated Learning with Client Selection via Submodular Optimization

Addressing Heterogeneity in Federated Learning with Client Selection via Submodular Optimization

Research article (ACM Transactions on Sensor Networks, 2023) · cited 21× · AI/ML
academia openalex:W4390105424
Press Enter · cited answer in seconds

Addressing Heterogeneity in Federated Learning with Client Selection via Submodular Optimization

Summary

Addressing Heterogeneity in Federated Learning with Client Selection via Submodular Optimization is a scholarly article[1].

Key Facts

  • Addressing Heterogeneity in Federated Learning with Client Selection via Submodular Optimization's instance of is recorded as scholarly article[2].

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

📑 Cite this page

Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). Addressing Heterogeneity in Federated Learning with Client Selection via Submodular Optimization. Retrieved May 24, 2026, from https://4ort.xyz/entity/addressing-heterogeneity-in-federated-learning-with-client-selection-via-submodular-optimization
MLA “Addressing Heterogeneity in Federated Learning with Client Selection via Submodular Optimization.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/addressing-heterogeneity-in-federated-learning-with-client-selection-via-submodular-optimization.
BibTeX @misc{4ortxyz_addressing-heterogeneity-in-federated-learning-with-client-selection-via-submodular-optimization_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Addressing Heterogeneity in Federated Learning with Client Selection via Submodular Optimization}}, year = {2026}, url = {https://4ort.xyz/entity/addressing-heterogeneity-in-federated-learning-with-client-selection-via-submodular-optimization}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Addressing Heterogeneity in Federated Learning with Client Selection via Submodular Optimization — https://4ort.xyz/entity/addressing-heterogeneity-in-federated-learning-with-client-selection-via-submodular-optimization (retrieved 2026-05-24)

Canonical URL: https://4ort.xyz/entity/addressing-heterogeneity-in-federated-learning-with-client-selection-via-submodular-optimization · Last refreshed: