Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data | Paper Citation Record

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This paper cites Optics: Ordering points to identify the clustering structure.

Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Optics: Ordering points to identify the clustering structure

Reference 1

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This paper cites Federated Learning with Personalization Layers.

Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Federated Learning with Personalization Layers

Reference 2

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Personalized and private peer-to-peer machine learning

Reference 3

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Federated learning with hierarchical clustering of local updates to improve training on non-IID data

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data LEAF: A Benchmark for Federated Settings

Reference 5

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This paper cites On Bridging Generic and Personalized Federated Learning for Image Classification.

Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data On Bridging Generic and Personalized Federated Learning for Image Classification

Reference 6

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This paper cites Federated Learning Of Out-Of-Vocabulary Words.

Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Federated Learning Of Out-Of-Vocabulary Words

Reference 7

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This paper cites Metafed: Federated learning among federations with cyclic knowledge distillation for personalized healthcare.

Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Metafed: Federated learning among federations with cyclic knowledge distillation for personalized healthcare

Reference 8

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data On robustness properties of convex risk minimization methods for pattern recognition

Reference 9

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This paper cites Characterizations of an em- pirical influence function for detecting influential cases in regression.

Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Characterizations of an em- pirical influence function for detecting influential cases in regression

Reference 10

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Residuals and influence in regression

Reference 11

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data H˚aler spr ˚aket together the Nordic countries? : a research report on young people’s f ¨ost˚aelse of Danish, Swedish and Norwegian

Reference 12

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This paper cites Adaptive Personalized Federated Learning.

Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Adaptive Personalized Federated Learning

Reference 13

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Flexible clustered federated learning for client-level data distribution shift

Reference 14

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data An Efficient Framework for Clustered Federated Learning

Reference 15

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This paper cites Adaptive aggregation weight assignment for federated learning: A deep reinforcement learning approach.

Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Adaptive aggregation weight assignment for federated learning: A deep reinforcement learning approach

Reference 16

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This paper cites Federated Learning of a Mixture of Global and Local Models.

Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Federated Learning of a Mixture of Global and Local Models

Reference 17

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This paper cites Federated Learning for Mobile Keyboard Prediction.

Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Federated Learning for Mobile Keyboard Prediction

Reference 18

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This paper cites FedML: A Research Library and Benchmark for Federated Machine Learning.

Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 19

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Deep residual learning for image recognition

Reference 20

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This paper cites The Non-IID Data Quagmire of Decentralized Machine Learning.

Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data The Non-IID Data Quagmire of Decentralized Machine Learning

Reference 21

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This paper cites Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification.

Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

Reference 22

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Fedrep: Towards horizontal federated load forecasting for retail energy providers

Reference 23

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Advances and open problems in federated learning

Reference 24

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Scaffold: Stochastic controlled averaging for federated learning

Reference 25

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Dynamic clustering in federated learning

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Understanding black-box predictions via influence functions

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Learning multiple layers of features from tiny images

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Federated learning with soft clustering

Reference 29

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data FedMD: Heterogenous Federated Learning via Model Distillation

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Federated optimization in heterogeneous networks

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data FedBN: Federated Learning on Non-IID Features via Local Batch Normalization

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Federated learning in mobile edge networks: A comprehensive survey

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Efficient approximation of cross-validation for kernel methods using bouligand influence function

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Multi-center federated learning: clients clustering for better personalization

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Layer-wised model aggregation for personalized federated learning

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Three Approaches for Personalization with Applications to Federated Learning

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Communication-efficient learning of deep networks from decentralized data

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data FedBABU: Towards Enhanced Representation for Federated Image Classification

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data A monolingual approach to contextualized word embeddings for mid- resource languages

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Clusterfl: A clustering-based federated learning system for human activity recognition

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Language models are unsupervised multitask learners

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Lia: Privacy-preserving data quality evaluation in fed- erated learning using a lazy influence approximation

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Pefll: Per- sonalized federated learning by learning to learn

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Per- sonalized federated learning using hypernetworks

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Federated Multi-Task Learning

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Personalized fed- erated learning with moreau envelopes

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Towards personalized federated learning

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data FL-bench: A federated learning benchmark for solving image classification tasks

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:54:43.609328Z

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This paper cites pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning.

Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Fedproto: Federated prototype learning across heterogeneous clients

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Fuzzy clustered federated learning under mixed data distributions

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data A survey on distributed machine learning

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data A Field Guide to Federated Optimization

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This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 56

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Personalized Federated Learning with Feature Alignment and Classifier Collaboration

Reference 57

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Toward understanding the influence of individual clients in federated learning

Reference 58

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Federated machine learning: Concept and applications

Reference 59

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Fedap: Adaptive personaliza- tion in federated learning for non-iid data

Reference 60

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Personalized Federated Learning with First Order Model Optimization

Reference 61

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Federated Learning with Non-IID Data

Reference 62

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Data-free knowledge distillation for heterogeneous federated learning

Reference 63

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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data The record was previously connected to the following departments: Swedish (0101001)

Reference 2005

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