Federated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As researchers try to support more machine learning models with different privacy-preserving approaches, there is a requirement in developing s…
Federated learning linked to mean-field games for large-scale learning.
problem Large-scale distributed and privacy-preserving learning algorithms.
method Established a connection between federated learning and mean-field games, presenting federated learning as a differential game.
result Properties of the equilibrium of the federated learning game were discussed.
Paper proposes FedPer to combat statistical heterogeneity in federated learning for personalized tasks.
problem Statistical heterogeneity in federated learning data degrades performance of traditional federated averaging.
method FedPer: a base + personalization layer approach for federated training of deep feedforward neural networks.
result FedPer effectively combats statistical heterogeneity in non-identical data partitions of CIFAR datasets and personalized image aesthetics datasets.
Paper tackles Byzantine attacks in Federated Learning by clustering and robustifying.
problem Adversarial attacks from Byzantine machines in Federated Learning.
method Iterative Federated Clustering Algorithm (IFCA) with trimmed mean and median aggregation.
result Improved convergence rate for strongly convex loss functions in Byzantine-Robust IFCA.
Paper introduces OARF benchmark suite for federated learning systems.
problem Limited diversity in federated learning benchmarks.
method Characterizes OARF benchmark suite with diverse data and applications.
result Federated learning can effectively increase end-to-end throughput.
Federated Extra-Trees protects privacy while improving machine learning performance.
problem Scattered data and privacy concerns in machine learning.
method Local differential privacy in federated trees model.
result Improved accuracy and robustness in federated machine learning.
This review explores federated learning for IoT data privacy.
problem Privacy risks and data transfer costs in IoT data analytics.
method Federated learning approach to protect privacy and reduce data transfer.
result Survey of methods for improving communication efficiency and privacy in IoT federated learning.
Sherpa.ai framework combines federated learning and differential privacy for edge AI services.
problem Protecting data privacy in edge AI services.
method Holistic federated learning and differential privacy approach with methodological guidelines.
result Demonstrated through classification and regression use cases.
Federated learning for data streams tackles real-time learning from IoT devices.
problem Efficiently learn from data streams generated by IoT devices and smartphones.
method Proposes a general federated learning algorithm for data streams using weighted empirical risk minimization.
result Demonstrates improved performance on various machine learning tasks compared to static dataset approaches.
LC-FL uses generative models to reduce communication costs in federated learning.
problem High communication costs and strict model homogeneity in federated learning.
method LC-FL employs generative models to transmit data and aggregate models.
result LC-FL reduces communication costs and supports heterogeneous models.
Federated learning collaborates clients to train models without sharing data.
problem Privacy and data sharing in machine learning.
method Central server orchestrates collaborative training of models on decentralized data.
result Recent advances and open problems in FL.
New federated learning method for structured models.
problem Limited federated learning for structured probabilistic models.
method Structured variational inference adapted for federated learning.
result Effective algorithms compared to hierarchical Bayesian models.
Locally adaptive federated learning improves convergence in distributed machine learning.
problem Balancing local updates in federated learning leads to slow convergence.
method Locally adaptive federated learning algorithms that use uncoordinated stepsizes based on local geometric information.
result Locally adaptive methods can be particularly efficient in overparameterized settings and outperform standard federated algorithms.
This paper surveys techniques to personalize federated learning models.
problem Personalized models outperform shared models for some clients, reducing participation.
method Surveys recent research on personalizing federated learning models.
result Personalization techniques improve model performance for individual clients.
FLBench automates federated learning benchmarking.
problem Manual dataset partitioning fails to simulate real-world isolated data islands.
method Develops a federated learning benchmark suite with three domains.
result Automates evaluation of federated learning systems and algorithms.
Surveying how to use unlabeled data in federated learning.
problem Costly labeling of data limits FL applications.
method Survey and analyze existing research.
result Potential for using unlabeled data in FL.
FedHDPrivacy uses DP to improve FL in IoT, maintaining high accuracy.
problem Privacy threats in FL, especially in IoT environments.
method Integrates DP with neuro-symbolic computing, actively monitoring and adjusting noise.
result Maintains high performance in manufacturing monitoring, surpassing other FL methods.
FTTQ optimizes quantized networks in federated learning, reducing communication costs.
problem Redundant parameters in full-precision models lead to excessive communication costs in federated learning.
method FTTQ algorithm that optimizes quantized networks on clients through self-learning quantization factors.
result FTTQ reduces communication costs and can achieve slightly better performance on non-IID data.
FPFL mitigates unfairness in private federated learning.
problem Differential privacy degrades model performance on under-represented groups.
method Extends modified method of differential multipliers to private federated learning.
result FPFL reduces unfairness in trained models on private federated learning.
Federated Learning solves privacy and data distribution challenges in machine learning.
problem Privacy concerns and distributed data in machine learning.
method Federated Learning framework to handle privacy and distributed data.
result Solves privacy and data distribution challenges in machine learning.
Paper proposes an adaptive gradient method for federated learning.
problem Communication efficiency in federated learning.
method Adaptive gradient method for federated learning.
result Guaranteed convergence and communication efficiency in federated learning.
Federated learning is a new machine learning paradigm which allows data parties to build machine learning models collaboratively while keeping their data secure and private. While research efforts on federated learning have been growing tremendously in the past two years, most existing works still depend on pre-existin…
Federated learning enables training collaborative machine learning models at scale with many participants whilst preserving the privacy of their datasets. Standard federated learning techniques are vulnerable to Byzantine failures, biased local datasets, and poisoning attacks. In this paper we introduce Adaptive Federa…
Adaptive optimizers improve federated learning performance.
problem Inadequate optimization methods in federated learning.
method Federated versions of adaptive optimizers (Adagrad, Adam, Yogi).
result Adaptive optimizers enhance federated learning performance.
A real-time federated neural architecture search approach reduces costs and improves performance.
problem High communication and computational demands in federated learning for large models.
method Evolutionary approach with double-sampling technique to optimize model performance and reduce costs.
result Effective real-time federated neural architecture search for deep models on edge devices.
Fog learning distributes ML model training across heterogeneous devices and networks.
problem Challenges with conventional federated learning in heterogeneous networks.
method Intelligent distribution of ML model training across nodes from edge devices to cloud servers.
result Enhanced federated learning with multi-layer hybrid framework considering network, heterogeneity, and proximity.
Federated learning performs distributed model training using local data hosted by agents. It shares only model parameter updates for iterative aggregation at the server. Although it is privacy-preserving by design, federated learning is vulnerable to noise corruption of local agents, as demonstrated in the previous stu…
Secure XGB for privacy-preserving machine learning in federated learning.
problem Privacy-preserving machine learning in federated learning with practical gradient tree boosting models.
method Secure multi-party computation, distributed model storage, secure permutation protocols.
result Our XGB models provide competitive accuracy and practical performance.
Three approaches for personalized models in machine learning.
problem Training a single model for all users is not optimal in many scenarios.
method User clustering, data interpolation, and model interpolation.
result Learning-theoretic guarantees and efficient algorithms for personalized models.
Intensive care data are valuable for improvement of health care, policy making and many other purposes. Vast amount of such data are stored in different locations, on many different devices and in different data silos. Sharing data among different sources is a big challenge due to regulatory, operational and security r…
Federated learning is a distributed form of machine learning where both the training data and model training are decentralized. In this paper, we use federated learning in a commercial, global-scale setting to train, evaluate and deploy a model to improve virtual keyboard search suggestion quality without direct access…
A novel Federated Learning scheme using Optimal Transport for personalized model training.
problem Training models with data from clients having non-identically distributed data.
method Personalized Federated Learning scheme based on Optimal Transport (FedOT).
result FedOT scheme effectively transfers data from multiple distributions to a common domain and optimizes the prediction model.
PVI combines federated learning and variational inference for probabilistic model training.
problem Federated learning's lack of probabilistic model uncertainty estimation.
method Partitioned variational inference (PVI) framework for federated probabilistic model training.
result PVI unifies fragmented literature and demonstrates effectiveness in various federated settings.
A framework for large-scale federated learning with non-IID data.
problem Stability of models trained on non-IID data in federated learning.
method Generation of non-IID datasets and modular evaluation framework.
result Open-source benchmark for large-scale federated learning research.
Federated Learning improves Firefox URL bar suggestions.
problem Improving user typing efficiency in the Firefox URL bar.
method Federated Learning for privacy-preserving model training.
result Users type over half a character less to find what they want.
FedCVT improves VFL models with limited aligned samples.
problem Limited aligned samples in VFL.
method Cross-view training to estimate missing features and predict pseudo-labels.
result FedCVT significantly outperforms vanilla VFL.
Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day. This wealth of data can help to learn models that can improve the user experience on each device. However, the scale and heterogeneity of federated data presents new …
FedSplit improves federated learning for heterogeneous data.
problem Data heterogeneity in federated learning degrades convergence and performance.
method FedSplit splits data into shared and personalized groups, optimizing a novel objective function.
result FedSplit converges faster and performs better than standard federated learning.
Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data. We have built a scalable production system for Federated Learning in the domain of mobile devices, based on TensorFlow. In this paper, we describe the resulting high-level design, sketch s…
This work provides bounds on generalization error and privacy leakage in federated learning.
problem Bounding generalization error and privacy leakage in federated learning.
method Information-theoretic framework for classical, distributed, and federated learning.
result Upper and lower bounds on generalization error and privacy leakage.
Enhances FL with domain adaptation for better model accuracy.
problem Improving model accuracy in Federated Learning.
method Domain adaptation for per-user model improvement in Federated Learning.
result Domain adaptation improves model accuracy for all users, especially under differential privacy constraints.
New algorithm solves federated minimax optimization problems.
problem Federated minimax optimization challenges.
method Federated Stochastic Smoothed Gradient Descent Ascent (FESS-GDA).
result FESS-GDA uniformly solves federated minimax problems.
Federated learning technique improves convergence speed with communication delays.
problem Communication delays between edge nodes and aggregator in federated learning.
method Developed FedDelAvg, a technique that generalizes federated averaging to incorporate a weighting between current local model and delayed global model.
result FedDelAvg achieves a significant improvement in convergence speed, especially when optimizing the weighting scheme to account for delays.
Paper analyzes and compares ELF algorithms for federated learning.
problem Improving efficiency and privacy in federated learning.
method Proposes P-ELF, D-ELF, and B-ELF algorithms with primal, dual, and bidirectional compression.
result Provides non-asymptotic convergence guarantees under Log-Sobolev inequality.
A new federated learning method reduces communication costs and improves adaptivity.
problem Large communication overhead and lack of adaptivity in federated learning.
method FedCAMS: A novel communication-efficient adaptive federated learning method with theoretical guarantees.
result FedCAMS achieves the same convergence rate as non-compressed federated learning methods.
Machine learning relies on the availability of a vast amount of data for training. However, in reality, most data are scattered across different organizations and cannot be easily integrated under many legal and practical constraints. In this paper, we introduce a new technique and framework, known as federated transfe…
Secure federated learning framework resists adversarial users.
problem Resilience against adversarial (Byzantine) users in federated learning.
method Integrated stochastic quantization, verifiable outlier detection, and secure model aggregation.
result First single-server Byzantine-resilient secure aggregation framework (BREA) for secure federated learning.
This work makes federated Bayesian learning differentially private.
problem Privacy concerns in federated learning with diverse data and computational constraints.
method Modified Partitioned Variational Inference (PVI) to ensure differential privacy.
result Moderately private logistic regression models can be learned in the federated setting with similar performance to non-privately trained models.