Personalized Federated Learning

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

7 leaderboard tables shown for this task, 7 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

5 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 117 papers with code (311 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Adaptive Personalized Federated Learning
  30 Mar 2020 11 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  Investigation of the degree of personalization in federated learning algorithms has shown that only maximizing the performance of the global model will confine the capacity of the local models to personalize.
• PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark
  8 Dec 2023 6 repositories listed Syntology ran 6 of 7 samples · 1 unverified · 1 pointer-only (licence)
  Amid the ongoing advancements in Federated Learning (FL), a machine learning paradigm that allows collaborative learning with data privacy protection, personalized FL (pFL)has gained significant prominence as a research…
• Federated Multi-Task Learning under a Mixture of Distributions
  23 Aug 2021 5 repositories listed Syntology ran 3 of 17 samples · 14 unverified · 1 pointer-only (licence)
  The increasing size of data generated by smartphones and IoT devices motivated the development of Federated Learning (FL), a framework for on-device collaborative training of machine learning models.
• GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning
  20 Aug 2023 4 repositories listed Syntology ran 9 of 17 samples · 8 unverified · 7 pointer-only (licence)
  Federated Learning (FL) is popular for its privacy-preserving and collaborative learning capabilities.
• FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy
  1 Jul 2023 4 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  To address this, we propose the Federated Conditional Policy (FedCP) method, which generates a conditional policy for each sample to separate the global information and personalized information in its features and then…
• Personalized Federated Learning with Feature Alignment and Classifier Collaboration
  20 Jun 2023 4 repositories listed Syntology ran 12 of 16 samples · 4 unverified · 10 pointer-only (licence)
  Data heterogeneity is one of the most challenging issues in federated learning, which motivates a variety of approaches to learn personalized models for participating clients.
• Ditto: Fair and Robust Federated Learning Through Personalization
  8 Dec 2020 4 repositories listed Syntology ran 15 of 18 samples · 3 unverified · 1 pointer-only (licence)
  Fairness and robustness are two important concerns for federated learning systems.
• Personalized Federated Learning with Moreau Envelopes
  16 Jun 2020 4 repositories listed
  Federated learning (FL) is a decentralized and privacy-preserving machine learning technique in which a group of clients collaborate with a server to learn a global model without sharing clients' data.
• FedALA: Adaptive Local Aggregation for Personalized Federated Learning
  2 Dec 2022 3 repositories listed Syntology ran 1 of 3 samples · 2 unverified
  A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client.
• On Bridging Generic and Personalized Federated Learning for Image Classification
  2 Jul 2021 3 repositories listed Syntology ran 1 of 2 samples · 1 unverified
  On the one hand, we introduce a family of losses that are robust to non-identical class distributions, enabling clients to train a generic predictor with a consistent objective across them.
• Exploiting Shared Representations for Personalized Federated Learning
  14 Feb 2021 3 repositories listed
  Based on this intuition, we propose a novel federated learning framework and algorithm for learning a shared data representation across clients and unique local heads for each client.
• Personalized Federated Learning with First Order Model Optimization
  15 Dec 2020 3 repositories listed Syntology ran 1 of 2 samples · 1 unverified · 1 pointer-only (licence)
  While federated learning traditionally aims to train a single global model across decentralized local datasets, one model may not always be ideal for all participating clients.
• Personalized Federated Learning via Feature Distribution Adaptation
  1 Nov 2024 2 repositories listed Syntology ran 3 of 4 samples · 1 unverified · 4 pointer-only (licence)
  Federated learning (FL) is a distributed learning framework that leverages commonalities between distributed client datasets to train a global model.
• Low-Resource Machine Translation through the Lens of Personalized Federated Learning
  18 Jun 2024 2 repositories listed
  We present a new approach called MeritOpt based on the Personalized Federated Learning algorithm MeritFed that can be applied to Natural Language Tasks with heterogeneous data.
• Federated Representation Learning in the Under-Parameterized Regime
  7 Jun 2024 2 repositories listed Syntology ran 6 of 8 samples · 2 unverified
  In this paper, we make the initial efforts to investigate FRL in the under-parameterized regime, where the FL model is insufficient to express the variations in all ground-truth models.
• FedAS: Bridging Inconsistency in Personalized Federated Learning
  1 Jan 2024 2 repositories listed
  In PFL clients update their shared parameters to communicate and learn from others while keeping personalized parts unchanged leading to poor coordination between these two components.
• PeFLL: Personalized Federated Learning by Learning to Learn
  8 Jun 2023 2 repositories listed
  We present PeFLL, a new personalized federated learning algorithm that improves over the state-of-the-art in three aspects: 1) it produces more accurate models, especially in the low-data regime, and not only for…
• Efficient Personalized Federated Learning via Sparse Model-Adaptation
  4 May 2023 2 repositories listed Syntology ran 5 of 6 samples · 1 unverified · 1 pointer-only (licence)
  To overcome these challenges, we propose a novel approach named pFedGate for efficient personalized FL by adaptively and efficiently learning sparse local models.
• Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
  18 Jun 2022 2 repositories listed
  To better answer these questions, we propose Motley, a benchmark for personalized federated learning.
• Personalized Federated Learning through Local Memorization
  17 Nov 2021 2 repositories listed Syntology ran 0 of 2 samples · 2 unverified · 2 pointer-only (licence)
  Federated learning allows clients to collaboratively learn statistical models while keeping their data local.
• Personalized Federated Learning using Hypernetworks
  8 Mar 2021 2 repositories listed
  In this approach, a central hypernetwork model is trained to generate a set of models, one model for each client.
• A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization
  14 Feb 2021 2 repositories listed
  Non-Independent and Identically Distributed (non- IID) data distribution among clients is considered as the key factor that degrades the performance of federated learning (FL).
• Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach
  1 Dec 2020 2 repositories listed
  In this paper, we study a personalized variant of the federated learning in which our goal is to find an initial shared model that current or new users can easily adapt to their local dataset by performing one or a few…
• pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models
  7 Jul 2025 1 repository listed Syntology ran 1 of 11 samples · 10 unverified · 11 pointer-only (licence)
  Vision-Language Models (VLMs) like CLIP have demonstrated remarkable generalization in zero- and few-shot settings, but adapting them efficiently to decentralized, heterogeneous data remains a challenge.
• Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive Mapping
  13 Jun 2025 1 repository listed
  Federated weakly supervised video anomaly detection represents a significant advancement in privacy-preserving collaborative learning, enabling distributed clients to train anomaly detectors using only video-level…
• FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcare
  15 Apr 2025 1 repository listed Syntology ran 4 of 4 samples · 0 unverified
  Federated Learning (FL) has emerged as an effective solution for multi-institutional collaborations without sharing patient data, offering a range of methods tailored for diverse applications.
• BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions in Federated learning
  9 Mar 2025 1 repository listed
  We propose BTFL, a Bayesian-based test-time generalization method for TGFL, which balances generalization and personalization at the sample level during testing.
• PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning
  1 Feb 2025 1 repository listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Federated learning (FL) has gained widespread attention for its privacy-preserving and collaborative learning capabilities.
• Calibre: Towards Fair and Accurate Personalized Federated Learning with Self-Supervised Learning
  28 Dec 2024 1 repository listed
  In the context of personalized federated learning, existing approaches train a global model to extract transferable representations, based on which any client could train personalized models with a limited number of…
• Federated Learning of Dynamic Bayesian Network via Continuous Optimization from Time Series Data
  13 Dec 2024 1 repository listed
  In this study, we first introduce a federated learning approach for estimating the structure of a Dynamic Bayesian Network from homogeneous time series data that are horizontally distributed across different parties.

Syntology lines on 17 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.