Federated Learning

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

12 leaderboard tables shown for this task, 12 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. 10 shown of 12 until expanded.

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

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

Subtasks archive 2025-07-28

5 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 1,815 papers with code (6,771 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.

• Communication-Efficient Learning of Deep Networks from Decentralized Data
  17 Feb 2016 41 repositories listed Syntology ran 40 of 77 samples · 37 unverified · 50 pointer-only (licence)
  Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device.
• Federated Optimization in Heterogeneous Networks
  14 Dec 2018 22 repositories listed Syntology ran 9 of 26 samples · 17 unverified · 9 pointer-only (licence)
  Theoretically, we provide convergence guarantees for our framework when learning over data from non-identical distributions (statistical heterogeneity), and while adhering to device-level systems constraints by allowing…
• 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.
• Advances and Open Problems in Federated Learning
  10 Dec 2019 9 repositories listed Syntology ran 0 of 9 samples · 9 unverified
  FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
• Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
  13 Sep 2019 9 repositories listed
  In this work, we look at the effect such non-identical data distributions has on visual classification via Federated Learning.
• Adaptive Federated Optimization
  29 Feb 2020 8 repositories listed Syntology ran 1 of 2 samples · 1 unverified
  Federated learning is a distributed machine learning paradigm in which a large number of clients coordinate with a central server to learn a model without sharing their own training data.
• SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
  14 Oct 2019 8 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  We obtain tight convergence rates for FedAvg and prove that it suffers from `client-drift' when the data is heterogeneous (non-iid), resulting in unstable and slow convergence.
• Inverting Gradients -- How easy is it to break privacy in federated learning?
  31 Mar 2020 7 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  The idea of federated learning is to collaboratively train a neural network on a server.
• FedMD: Heterogenous Federated Learning via Model Distillation
  8 Oct 2019 7 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  With 10 distinct participants, the final test accuracy of each model on average receives a 20% gain on top of what's possible without collaboration and is only a few percent lower than the performance each model would…
• Towards Federated Learning at Scale: System Design
  4 Feb 2019 7 repositories listed Syntology ran 0 of 9 samples · 9 unverified
  Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data.
• Agnostic Federated Learning
  1 Feb 2019 7 repositories listed Syntology ran 3 of 10 samples · 7 unverified
  A key learning scenario in large-scale applications is that of federated learning, where a centralized model is trained based on data originating from a large number of clients.
• LEAF: A Benchmark for Federated Settings
  3 Dec 2018 7 repositories listed Syntology ran 1 of 3 samples · 2 unverified
  Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day.
• 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…
• Model-Contrastive Federated Learning
  30 Mar 2021 6 repositories listed Syntology ran 2 of 9 samples · 7 unverified
  A key challenge in federated learning is to handle the heterogeneity of local data distribution across parties.
• Federated Learning for Mobile Keyboard Prediction
  8 Nov 2018 6 repositories listed Syntology ran 0 of 11 samples · 11 unverified
  We train a recurrent neural network language model using a distributed, on-device learning framework called federated learning for the purpose of next-word prediction in a virtual keyboard for smartphones.
• Differentially Private Federated Learning: A Client Level Perspective
  20 Dec 2017 6 repositories listed
  In such an attack, a client's contribution during training and information about their data set is revealed through analyzing the distributed model.
• Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
  5 Dec 2017 6 repositories listed
  The situation gets even worse with distributed training on mobile devices (federated learning), which suffers from higher latency, lower throughput, and intermittent poor connections.
• Robust Federated Learning With Noisy and Heterogeneous Clients
  1 Jan 2022 5 repositories listed
  Model heterogeneous federated learning is a challenging task since each client independently designs its own model.
• Federated Learning Based on Dynamic Regularization
  8 Nov 2021 5 repositories listed Syntology ran 6 of 15 samples · 9 unverified · 9 pointer-only (licence)
  We propose a novel federated learning method for distributively training neural network models, where the server orchestrates cooperation between a subset of randomly chosen devices in each round.
• 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.
• FedML: A Research Library and Benchmark for Federated Machine Learning
  27 Jul 2020 5 repositories listed Syntology ran 0 of 5 samples · 5 unverified · 1 pointer-only (licence)
  Federated learning (FL) is a rapidly growing research field in machine learning.
• Federated Optimization for Heterogeneous Networks
  16 May 2019 5 repositories listed
  Federated learning involves training and effectively combining machine learning models from distributed partitions of data (i.
• 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.
• Few-Shot Model Agnostic Federated Learning
  10 Oct 2022 4 repositories listed
  To address these issues, this paper presents a novel framework with two main parts: 1) model agnostic federated learning, it performs public-private communication by unifying the model prediction outputs on the shared…
• Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data
  14 Jul 2021 4 repositories listed
  Federated learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data.
• Data-Free Knowledge Distillation for Heterogeneous Federated Learning
  20 May 2021 4 repositories listed
  Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global server iteratively averages the model parameters of local users without accessing their data.
• FedProto: Federated Prototype Learning across Heterogeneous Clients
  1 May 2021 4 repositories listed
  Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occurs in the gradient space.
• Robust Federated Learning by Mixture of Experts
  23 Apr 2021 4 repositories listed
  We present a novel weighted average model based on the mixture of experts (MoE) concept to provide robustness in Federated learning (FL) against the poisoned/corrupted/outdated local models.

Syntology lines on 20 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.