A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation | Paper Citation Record

Observation d55ee603-4ecb-45e9-a6fb-47191b58da3b · outbound

This paper cites Daisyrec 2.0: Benchmarking recommendation for rigorous evaluation,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Daisyrec 2.0: Benchmarking recommendation for rigorous evaluation,

Reference 1

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Observation 416bf402-d4dd-4f85-9e92-d6eb9b80b707 · outbound

This paper cites Adaptive fair representation learning for personalized fairness in recommendations via information align- ment,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Adaptive fair representation learning for personalized fairness in recommendations via information align- ment,

Reference 2

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This paper cites Mitigating confounding bias in practical recommender systems with partially inaccessible exposure status,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Mitigating confounding bias in practical recommender systems with partially inaccessible exposure status,

Reference 3

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This paper cites A survey on federated recommendation systems,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation A survey on federated recommendation systems,

Reference 4

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This paper cites Horizontal federated recommender system: A survey,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Horizontal federated recommender system: A survey,

Reference 5

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This paper cites Communication-efficient learning of deep networks from decentralized data,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Communication-efficient learning of deep networks from decentralized data,

Reference 6

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Observation 79fdd0de-9cbd-49b3-8ef4-50496968cdb8 · outbound

This paper cites Federated learning for generalization, robustness, fairness: A survey and benchmark,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Federated learning for generalization, robustness, fairness: A survey and benchmark,

Reference 7

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Observation 5737e4db-1a1a-45de-86bd-c30144f64ce8 · outbound

This paper cites Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System

Reference 8

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This paper cites Fedgnn: Federated graph neural network for privacy-preserving recommendation,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Fedgnn: Federated graph neural network for privacy-preserving recommendation,

Reference 9

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This paper cites Lightfr: Lightweight federated recommendation with privacy-preserving matrix factorization,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Lightfr: Lightweight federated recommendation with privacy-preserving matrix factorization,

Reference 10

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This paper cites Personalized latent structure learning for recommendation,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Personalized latent structure learning for recommendation,

Reference 11

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Observation 2fe7e2f9-96bc-4621-b1d6-641a0241cbb6 · outbound

This paper cites A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation,

Reference 12

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This paper cites Language representations can be what recommenders need: Findings and potentials,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Language representations can be what recommenders need: Findings and potentials,

Reference 13

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This paper cites Secure federated matrix factorization,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Secure federated matrix factorization,

Reference 14

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This paper cites Federated neural collaborative filter- ing,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Federated neural collaborative filter- ing,

Reference 15

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This paper cites Dual personalization on federated recommendation,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Dual personalization on federated recommendation,

Reference 16

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This paper cites Marking the pace: A blockchain-enhanced privacy-traceable strategy for federated recom- mender systems,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Marking the pace: A blockchain-enhanced privacy-traceable strategy for federated recom- mender systems,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:43.432575Z

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This paper cites Personalized federated recommendation via joint representation learning, user clustering, and model adaptation,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Personalized federated recommendation via joint representation learning, user clustering, and model adaptation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:43.224181Z

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Observation 2a9d3a9d-aac3-4832-98e6-bb5798c0e2e8 · outbound

This paper cites Cluster-driven personalized federated recommendation with interest-aware graph convolution network for multimedia,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Cluster-driven personalized federated recommendation with interest-aware graph convolution network for multimedia,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:43.018946Z

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Observation 49db98f5-11ce-4172-b20d-7d19324ca51e · outbound

This paper cites Federated recommendation with additive personalization,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Federated recommendation with additive personalization,

Reference 20

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This paper cites Gpfedrec: Graph-guided personalization for federated recommendation,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Gpfedrec: Graph-guided personalization for federated recommendation,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:42.588784Z

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Observation 331855b6-3b65-4976-b7c3-feff7d7f6cc6 · outbound

This paper cites Disentangled representation learning for recommendation,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Disentangled representation learning for recommendation,

Reference 22

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This paper cites Are graph augmentations necessary? simple graph contrastive learning for recommendation,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Are graph augmentations necessary? simple graph contrastive learning for recommendation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:42.136909Z

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This paper cites Contrastive learning for representation degeneration problem in sequential recommendation,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Contrastive learning for representation degeneration problem in sequential recommendation,

Reference 24

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This paper cites Recdcl: Dual contrastive learning for recommendation,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Recdcl: Dual contrastive learning for recommendation,

Reference 25

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This paper cites Understand- ing and mitigating dimensional collapse in federated learning,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Understand- ing and mitigating dimensional collapse in federated learning,

Reference 26

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This paper cites Relaxed contrastive learning for federated learning,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Relaxed contrastive learning for federated learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:41.622414Z

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This paper cites Fedloge: Joint local and generic federated learning under long-tailed data,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Fedloge: Joint local and generic federated learning under long-tailed data,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:41.374749Z

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This paper cites Mitigating the popularity bias of graph collaborative filtering: A dimensional collapse perspective,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Mitigating the popularity bias of graph collaborative filtering: A dimensional collapse perspective,

Reference 29

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This paper cites Lower bounds and optimal algorithms for personalized federated learning,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Lower bounds and optimal algorithms for personalized federated learning,

Reference 30

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This paper cites Personalized federated learning under mixture of distribu- tions,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Personalized federated learning under mixture of distribu- tions,

Reference 31

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This paper cites Personalized federated learning on non-iid data via group-based meta-learning,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Personalized federated learning on non-iid data via group-based meta-learning,

Reference 33

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This paper cites Fedbabu: Toward enhanced representation for federated image classification,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Fedbabu: Toward enhanced representation for federated image classification,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:40.015777Z

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This paper cites Fedpop: A bayesian approach for personalised federated learning,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Fedpop: A bayesian approach for personalised federated learning,

Reference 35

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Observation c53eacea-f6f1-4b98-acf1-c79bff719410 · outbound

This paper cites A statistical framework for personalized federated learning and estimation: Theory, algorithms, and privacy,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation A statistical framework for personalized federated learning and estimation: Theory, algorithms, and privacy,

Reference 36

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Observation 720f8f27-9365-4b32-a08c-a2d4dea67436 · outbound

This paper cites Adaptive Personalized Federated Learning.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Adaptive Personalized Federated Learning

Reference 37

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

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach,

Reference 38

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Observation a2a723c6-1cfb-4a5b-9e02-28add274b1e3 · outbound

This paper cites Exploiting shared representations for personalized federated learning,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Exploiting shared representations for personalized federated learning,

Reference 39

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Observation b4bae32f-5d9c-4571-9af1-31065ad00c2d · outbound

This paper cites Heterogeneous personalized federated learning by local-global updates mixing via convergence rate,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Heterogeneous personalized federated learning by local-global updates mixing via convergence rate,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:39.196996Z

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This paper cites Refer: Retrieval-enhanced vertical federated recommenda- tion for full set user benefit,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Refer: Retrieval-enhanced vertical federated recommenda- tion for full set user benefit,

Reference 41

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Observation 09f20e55-bfdb-4292-860b-92acf138ebde · outbound

This paper cites Towards efficient communication and secure federated recommendation system via low-rank training,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Towards efficient communication and secure federated recommendation system via low-rank training,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:38.744838Z

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Observation fb9b7510-016e-4610-b0d9-b71df9a6f087 · outbound

This paper cites Fine-grained preference-aware personalized federated poi recommendation with data sparsity,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Fine-grained preference-aware personalized federated poi recommendation with data sparsity,

Reference 43

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Observation b487138a-dcef-4cd7-99fa-909485fa8d56 · outbound

This paper cites Privacy-preserving sequential recommendation with collaborative confusion,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Privacy-preserving sequential recommendation with collaborative confusion,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:38.299660Z

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Observation de70dc44-6621-4ca1-aee5-b3c4dccf5710 · outbound

This paper cites Hfsa: A semi-asynchronous hierarchical federated recommendation system in smart city,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Hfsa: A semi-asynchronous hierarchical federated recommendation system in smart city,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:38.051741Z

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This paper cites Fedcore: Federated learning for cross-organization recommen- dation ecosystem,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Fedcore: Federated learning for cross-organization recommen- dation ecosystem,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:37.816384Z

Source-reported events for the cited work

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Observation be700fa6-87b6-40aa-9f63-a5ada504c197 · outbound

This paper cites Federated Graph Learning for Cross-Domain Recommendation.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Federated Graph Learning for Cross-Domain Recommendation

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:44:32.996146Z

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Observation a208d689-d788-4b3c-a10c-05057ebe7c9c · outbound

This paper cites FedCSR: A federated framework for multi-platform cross-domain sequential recommendation with dual contrastive learning,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation FedCSR: A federated framework for multi-platform cross-domain sequential recommendation with dual contrastive learning,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:37.615792Z

Source-reported events for the cited work

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Observation cb22233b-428c-4a24-b243-8432baa65045 · outbound

This paper cites Not one less: Exploring interplay between user profiles and items in untargeted attacks against federated recommendation,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Not one less: Exploring interplay between user profiles and items in untargeted attacks against federated recommendation,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:37.363498Z

Source-reported events for the cited work

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Observation 801a3b77-ff42-48f6-81dc-b3cdc5ad0777 · outbound

This paper cites Defending against membership inference attack for counterfactual federated recommendation with differentially private representation learning,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Defending against membership inference attack for counterfactual federated recommendation with differentially private representation learning,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:37.152260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 26d10ddd-cd6e-4c68-9d4c-4cf961d79bd6 · outbound

This paper cites Federated recommender system based on diffusion augmentation and guided denoising,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Federated recommender system based on diffusion augmentation and guided denoising,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:36.915036Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a7032a43-70bc-4461-9fcf-b78f8743b055 · outbound

This paper cites On the embedding collapse when scaling up recommendation models,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation On the embedding collapse when scaling up recommendation models,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:36.757061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9bf8b989-b960-4a2b-867f-cdb9620e037a · outbound

This paper cites Neural tangent kernel: conver- gence and generalization in neural networks,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Neural tangent kernel: conver- gence and generalization in neural networks,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:36.585879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3357bb7e-2d98-49ee-892c-0d6866c9eb3d · outbound

This paper cites On exact computation with an infinitely wide neural net,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation On exact computation with an infinitely wide neural net,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:36.366087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b459d1c1-021f-4510-ba94-25fca92db4bf · outbound

This paper cites Generalized leverage score sampling for neural networks,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Generalized leverage score sampling for neural networks,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:36.084367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bc95be68-834e-4b56-98e2-3dbc964e0005 · outbound

This paper cites Gradient descent provably optimizes over-parameterized neural networks,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Gradient descent provably optimizes over-parameterized neural networks,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:35.882593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 07729532-b3ea-4d80-983b-6c9012c5ff37 · outbound

This paper cites Dynamics of deep neural networks and neural tangent hierarchy,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Dynamics of deep neural networks and neural tangent hierarchy,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:35.643457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ada2e596-3c7e-4c5e-aa68-8cd53dc0fa72 · outbound

This paper cites Over- parameterized adversarial training: An analysis overcoming the curse of,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Over- parameterized adversarial training: An analysis overcoming the curse of,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:35.391837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 115b28cf-af72-47e0-ae3a-1a8b01634671 · outbound

This paper cites Understanding contrastive representation learning through alignment and uniformity on the hypersphere,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Understanding contrastive representation learning through alignment and uniformity on the hypersphere,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:35.188647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f93c3322-6003-44e8-924e-25987ed18440 · outbound

This paper cites Noise-contrastive estimation: A new estimation principle for unnormalized statistical models,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Noise-contrastive estimation: A new estimation principle for unnormalized statistical models,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:34.987041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 07a4d8ac-24ae-4270-b7fa-804c89795a3e · outbound

This paper cites Barlow twins: Self-supervised learning via redundancy reduction,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Barlow twins: Self-supervised learning via redundancy reduction,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:34.759559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1f1f7b57-8cc1-4585-886e-3ef3738b2080 · outbound

This paper cites Zero-cl: Instance and feature decorrelation for negative-free symmetric contrastive learning,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Zero-cl: Instance and feature decorrelation for negative-free symmetric contrastive learning,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:34.554531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3b5a4c42-4d2a-4c4c-8253-3a1054385a5d · outbound

This paper cites Cl4ctr: A contrastive learning framework for ctr prediction,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Cl4ctr: A contrastive learning framework for ctr prediction,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:34.373800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 406ae84a-a71d-456c-bafe-9541310002bc · outbound

This paper cites Neural collaborative filtering,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Neural collaborative filtering,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:34.192494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bf5a6fe0-9e93-4d82-a003-722b6a18776b · outbound

This paper cites Item-based top-n recommendation algorithms,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Item-based top-n recommendation algorithms,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:33.985505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1d861957-0a43-47d4-bd0b-05768aa9303b · outbound

This paper cites Trirank: Review-aware explainable recommendation by modeling aspects,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Trirank: Review-aware explainable recommendation by modeling aspects,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:33.761990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8adaa48e-4470-40f7-b21e-0a44f243baf3 · outbound

This paper cites A stochastic approximation method,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation A stochastic approximation method,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:33.586127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d2afc73b-a4ce-496c-a8fa-779009e72027 · outbound

This paper cites An exponential learning rate schedule for deep learning,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation An exponential learning rate schedule for deep learning,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:33.409349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a7fc0fbb-1311-4e86-97c9-1d708763e54c · outbound

This paper cites Visualizing data using t-sne,.

A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation Visualizing data using t-sne,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:44:33.190535Z

Source-reported events for the cited work

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