Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling | Paper Citation Record

Observation f3b59014-f1cd-4daf-b987-e0b1865cd813 · outbound

This paper cites Ditto: Fair and robust federated learning through personalization,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Ditto: Fair and robust federated learning through personalization,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:04.146177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0e710ba8-ee27-46e4-99e0-2bfde67bf0f2 · outbound

This paper cites Over-the-air clustered federated learning,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Over-the-air clustered federated learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:04.025977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 29a19a95-1642-4ae3-a3b0-2aaff4a76c99 · outbound

This paper cites User-centric federated learning: Trading off wireless resources for personalization,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling User-centric federated learning: Trading off wireless resources for personalization,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:03.739808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7283b306-9b48-4ba4-8841-20c4ad984b84 · outbound

This paper cites Ensemble federated learning with non-iid data in wireless networks,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Ensemble federated learning with non-iid data in wireless networks,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:03.638518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fb494f5f-6d68-4d57-a703-9ef6cd4d2f63 · outbound

This paper cites Hierarchical personalized federated learning over massive mobile edge computing networks,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Hierarchical personalized federated learning over massive mobile edge computing networks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:03.418930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f7bf7f4e-43b2-4e6f-b73a-693d813a91e0 · outbound

This paper cites FedMD: Heterogenous federated learning via model distillation,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling FedMD: Heterogenous federated learning via model distillation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:03.246753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b7c0620d-5416-485a-ad23-42ff4d70a040 · outbound

This paper cites Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:03.129614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7a7ad3b2-593b-4a6a-8908-b3bd1c0cfb50 · outbound

This paper cites Personalized federated learning with differ- ential privacy and convergence guarantee,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Personalized federated learning with differ- ential privacy and convergence guarantee,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:02.976278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 705bb1b6-6340-4c3d-bd9d-73821cc313cf · outbound

This paper cites Semi-synchronous personalized federated learning over mobile edge networks,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Semi-synchronous personalized federated learning over mobile edge networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:02.786755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 92a16ec7-2be2-409d-834f-91f62c09dabb · outbound

This paper cites Personalized federated learning with moreau envelopes,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Personalized federated learning with moreau envelopes,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:02.639826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c642dbf5-814c-4e0a-8dfc-cec7463766ab · outbound

This paper cites Federated optimization in heterogeneous networks,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Federated optimization in heterogeneous networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:02.471857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1a1aadb5-e302-417b-9d96-30f1a7cf393a · outbound

This paper cites Personalized cross-silo federated learning on non-IID data,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Personalized cross-silo federated learning on non-IID data,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:02.297426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 33bc0b2e-9a53-4cd5-97cf-39e2571078c6 · outbound

This paper cites Adapt to adaptation: Learning personalization for cross-silo federated learning,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Adapt to adaptation: Learning personalization for cross-silo federated learning,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T11:31:54.144523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfa3c2a0-7f6e-446f-9068-cbb1902a01e9 · outbound

This paper cites FedALA: Adaptive local aggregation for personalized federated learning,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling FedALA: Adaptive local aggregation for personalized federated learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:02.152430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation def04ff0-8419-41c5-a079-30e0eea4375c · outbound

This paper cites Federated multi-task learning with non- stationary and heterogeneous data in wireless networks,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Federated multi-task learning with non- stationary and heterogeneous data in wireless networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:02.012360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8816fe73-2918-45eb-94f6-adc0cc187538 · outbound

This paper cites Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportu- nities,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportu- nities,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:01.822161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8b7c098b-037a-4ca9-b182-85c15508843a · outbound

This paper cites A joint learning and communications framework for federated learning over wireless networks,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling A joint learning and communications framework for federated learning over wireless networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:01.662906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation df447a9a-e383-42f8-9c4b-9bc460a95feb · outbound

This paper cites Integrating over-the-air federated learning and non-orthogonal multiple access: What role can ris play?.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Integrating over-the-air federated learning and non-orthogonal multiple access: What role can ris play?

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:01.496077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e47e0be8-7466-45d7-bea6-e70501edb335 · outbound

This paper cites Star-ris integrated nonorthogonal multiple access and over-the-air federated learning: Framework, analysis, and optimization,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Star-ris integrated nonorthogonal multiple access and over-the-air federated learning: Framework, analysis, and optimization,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:01.349503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation edd98052-133b-45cc-b7e7-b0489e0fd024 · outbound

This paper cites Semi-federated learning for collaborative intelligence in massive iot networks,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Semi-federated learning for collaborative intelligence in massive iot networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:01.233087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1f95e9a4-9410-4c94-ac1e-ae02dd22682c · outbound

This paper cites Deep learning with differential privacy,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Deep learning with differential privacy,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:01.091594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ff18d908-3f30-4d27-8ae5-dce4591df351 · outbound

This paper cites Federated learning with differential pri- vacy: Algorithms and performance analysis,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Federated learning with differential pri- vacy: Algorithms and performance analysis,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:00.979727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2f5e6ddf-e5f8-44d7-9ebc-21b107b7613e · outbound

This paper cites Local differential privacy-based federated learning for internet of things,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Local differential privacy-based federated learning for internet of things,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:00.799620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ed24a7a1-532a-440e-9d7a-ed90985f714b · outbound

This paper cites LDP-Fed: Federated learning with local differential privacy,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling LDP-Fed: Federated learning with local differential privacy,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:00.600297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 613c3fee-1232-4b31-b1a6-5be8fa08bc79 · outbound

This paper cites Amplitude-varying perturbation for balancing privacy and utility in federated learning,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Amplitude-varying perturbation for balancing privacy and utility in federated learning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:00.472176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9a5d39ca-6719-402d-b4cf-8d0fd117be27 · outbound

This paper cites Differentially private over-the-air federated learning over MIMO fading channels,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Differentially private over-the-air federated learning over MIMO fading channels,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:00.310839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f0155730-6a00-4a3b-9116-f89e3c9a42aa · outbound

This paper cites FedDual: Pair-wise gossip helps federated learning in large decentralized networks,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling FedDual: Pair-wise gossip helps federated learning in large decentralized networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:00.123483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b614407f-a83f-4990-812f-3736bb67a255 · outbound

This paper cites Joint privacy enhancement and quantization in federated learning,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Joint privacy enhancement and quantization in federated learning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:31:59.990999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5660c0aa-1094-463c-b1c0-666fc5867a79 · outbound

This paper cites Secure and efficient federated learning with provable performance guarantees via stochastic quantization,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Secure and efficient federated learning with provable performance guarantees via stochastic quantization,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:31:59.839552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 59b9a5d5-9f3a-4ae7-a089-42b21fc99ff0 · outbound

This paper cites P2cefl: Privacy-preserving and commu- nication efficient federated learning with sparse gradient and dithering quantization,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling P2cefl: Privacy-preserving and commu- nication efficient federated learning with sparse gradient and dithering quantization,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:31:59.702904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d311a497-3e56-4add-af08-e2c95c618d9a · outbound

This paper cites Towards personalized federated learning,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Towards personalized federated learning,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:31:59.505613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 603bbdf9-80ae-4bf2-ba38-10d5f312cf07 · outbound

This paper cites On privacy and personalization in cross- silo federated learning,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling On privacy and personalization in cross- silo federated learning,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:31:59.373150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c634b799-8b6d-4175-88fd-5a5cef7e2c85 · outbound

This paper cites Differentially private federated multi-task learning framework for enhancing human-to-virtual connec- tivity in human digital twin,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Differentially private federated multi-task learning framework for enhancing human-to-virtual connec- tivity in human digital twin,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:31:59.187340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 69689ca0-b48d-42e8-b061-37005b00f199 · outbound

This paper cites Personalized federated learning with differential privacy,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Personalized federated learning with differential privacy,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:31:59.012787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4ad059db-b07c-4936-81ae-d1b00c05e99f · outbound

This paper cites Fair resource allocation in federated learning,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Fair resource allocation in federated learning,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:31:58.837263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 42cf3eca-0d2d-453b-932f-8f5de9380fdc · outbound

This paper cites Federated learning meets multi- objective optimization,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Federated learning meets multi- objective optimization,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:31:58.652343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0978da0e-aad2-445a-a9f8-9f8b5314542a · outbound

This paper cites The algorithmic foundations of differential privacy,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling The algorithmic foundations of differential privacy,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T11:31:56.243942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 841cc070-d584-4879-94fe-59cdd4d9420a · outbound

This paper cites On the general BER expression of one-and two- dimensional amplitude modulations,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling On the general BER expression of one-and two- dimensional amplitude modulations,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:31:58.488370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0276f979-38d5-4474-8ed1-f7ee6fb1d59a · outbound

This paper cites Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:31:58.324654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 744724e7-7365-48dd-9063-1b2583f4a6cb · outbound

This paper cites Model inversion attacks that exploit confidence information and basic countermeasures,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Model inversion attacks that exploit confidence information and basic countermeasures,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T11:31:56.500377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4017f54-05f5-4960-a40b-25603b22a92a · outbound

This paper cites Linear convergence of gradient and proximal-gradient methods under the polyak-łojasiewicz condition,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Linear convergence of gradient and proximal-gradient methods under the polyak-łojasiewicz condition,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:31:58.127808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c91d1fbf-8ea1-407a-9c53-d83f68211b33 · outbound

This paper cites O’Searcoid,Metric spaces.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling O’Searcoid,Metric spaces

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:31:57.942558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ab022ce8-9758-45a9-8b44-1d3b45642372 · outbound

This paper cites The Hungarian method for the assignment problem,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling The Hungarian method for the assignment problem,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:31:57.782649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1742a7a2-7662-41f8-9342-1df773319d29 · outbound

This paper cites Jungnickel and D.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Jungnickel and D

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:31:57.634829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1076e966-0541-4735-a9a1-71d0dcaecda8 · outbound

This paper cites Joint computation offloading and trajectory planning for uav-assisted edge computing,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Joint computation offloading and trajectory planning for uav-assisted edge computing,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:31:57.482222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ff6d66f2-d98b-40d0-9871-77793c33f2f0 · outbound

This paper cites Adaptive federated learning in resource constrained edge computing systems,.

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Adaptive federated learning in resource constrained edge computing systems,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:31:57.336568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e19e2265-3147-4e91-aa79-1e88db13271c · outbound

This paper cites Likewise, (64) is based on (63).

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling Likewise, (64) is based on (63)

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:31:57.177133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers