Federated learning allows edge devices to collaboratively learn a shared model while keeping the training data on device, decoupling the ability to do model training from the need to store the data in the cloud. We propose Federated matched averaging (FedMA) algorithm designed for federated learning of modern neural ne…
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48 results for “federated matching”
Sharp bounds established for Federated Averaging (FedAvg), improving convergence rates.
problem Undetermined convergence rate of Federated Averaging (FedAvg) in Federated Learning.
method Developed novel iterate bias concept and proved sharp bounds on it, leading to improved convergence results.
result Lower bounds for FedAvg match existing upper bounds, showing no improvable capacity.
Federated learning is a distributed, privacy-aware learning scenario which trains a single model on data belonging to several clients. Each client trains a local model on its data and the local models are then aggregated by a central party. Current federated learning methods struggle in cases with heterogeneous client-…
Backdoor attacks can poison federated meta-learning models, even with few examples.
problem Backdoor attacks on federated meta-learning models.
method Proposed a defense mechanism inspired by matching networks.
result Backdoor attacks can be very successful and persist after additional training in federated meta-learning.
New method for semi-supervised learning in federated learning with and without labels at clients.
problem Training federated learning models with partially or completely unlabeled data.
method Federated Matching (FedMatch) with inter-client consistency loss and disjoint learning.
result FedMatch outperforms local semi-supervised learning and naive federated learning combinations.
FedForest adapts RF for federated learning, improving performance and efficiency.
problem Adapting RF for federated learning with heterogeneous data.
method FedForest uses a novel splitting procedure to aggregate client statistics, allowing non-parametric personalization.
result FedForest's federated RF achieves performance close to centralized models while being communication-efficient.
Paper studies user-level differential privacy in federated linear contextual bandits.
problem Federated learning with user-level differential privacy constraints.
method Unified federated bandits framework, CDP and LDP definitions, ROBIN algorithm.
result Near-optimal learning under user-level CDP with privacy budget and number of clients.
A novel federated learning framework resolves structural misalignment in model fusion.
problem Structural misalignment in model fusion due to chaotic information distribution.
method Feature-oriented regulation method (-Net) to ensure feature information allocation and dedicated collaboration schemes.
result Effective enhancement of federated learning applicability to heterogeneous settings with improved convergence speed, accuracy, and efficiency.
New algorithms solve nonconvex federated learning problems efficiently.
problem Nonconvex federated composite optimization in federated learning.
method FedDR and asyncFedDR algorithms combining Douglas-Rachford splitting, randomized block-coordinate strategies, and asynchronous implementation.
result Match communication complexity lower bound up to a constant factor.
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.
New federated learning protocols improve on knowledge distillation's poor performance.
problem Designing a universal API for federated learning without public data.
method Proposed Federated Kernel ridge regression using knowledge distillation.
result Performance of new protocols closely matches theoretical predictions.
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.
A new method for federated learning with only positive labels.
problem Learning with only positive labels leads to poor classifier performance.
method Federated Averaging with Spreadout (FedAwS) framework.
result FedAwS can almost match the performance of conventional learning with negative labels.
Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agentscs.LG
Federated UCBVI reduces communication costs while minimizing regret in multi-agent settings.
problem Minimizing regret in federated learning with heterogeneous agents.
method Federated Upper Confidence Bound Value Iteration (Fed-UCBVI) algorithm.
result Regret bound scales as with small additional term due to heterogeneity.
A new federated learning method clusters users into multiple models for better data distribution handling.
problem Non-IID data from heterogeneous sources in federated learning.
method Proposes a multi-center aggregation mechanism to learn multiple global models and optimally match users to centers.
result Our method outperforms existing federated learning methods on benchmark datasets.
FMP sampling improves model calibration without sharing data.
problem Difficulties in specifying priors for modern neural networks.
method FMP sampling replaces prior and likelihood with a predictive distribution, running the sampler centrally.
result FMP sampling significantly improves calibration over baseline methods.
Deep learning models for semantic segmentation of images require large amounts of data. In the medical imaging domain, acquiring sufficient data is a significant challenge. Labeling medical image data requires expert knowledge. Collaboration between institutions could address this challenge, but sharing medical data to…
Bayesian mixture models inference in federated learning
problem Inference of Bayesian mixture models in federated learning
method Consensus Monte Carlo approach
result Recovery of small clusters with greater accuracy than standard MCMC
Federated learning (FL) is a privacy-preserving paradigm for training collective machine learning models with locally stored data from multiple participants. Vertical federated learning (VFL) deals with the case where participants sharing the same sample ID space but having different feature spaces, while label informa…
Federated learning supports exact support recovery with minimal communication.
problem Learning the exact support of sparse linear regression in federated learning.
method One-shot communication algorithm for exact support recovery without optimization.
result Polynomial sample complexity and logarithmic number of clients required.
We present a federated, asynchronous, and -differentially private algorithm for PCA in the memory-limited setting. Our algorithm incrementally computes local model updates using a streaming procedure and adaptively estimates its leading principal components when only memory is av…
Unified analysis of federated learning with compression for various data distributions.
problem Communication overhead in federated learning with unreliable or limited communication.
method Periodic compressed communication and local gradient tracking schemes.
result Sharp convergence rates for various objective functions and data distributions.
Analyzes gaming in federated learning systems and provides design principles.
problem Gaming and cooperation in federated learning systems under partial observability.
method Developed an analytical framework to separate welfare-improving behavior from metric gaming.
result Derived threshold conditions for deterring harmful gaming and preserving cooperation.
Paper studies federated nonparametric testing with privacy constraints, achieving optimal rates and adaptive testing.
problem Federated nonparametric goodness-of-fit testing under distributed differential privacy constraints.
method Establishes matching lower and upper bounds on minimax separation rate, constructs adaptive testing procedure.
result Achieves optimal rates and demonstrates phase transition phenomena in federated testing.
A system for federated learning with private data, adding discrete Gaussian noise and secure aggregation.
problem Training models on private data distributed across devices while ensuring privacy.
method Discretizes data, adds discrete Gaussian noise, and uses secure aggregation to protect privacy.
result Matches the accuracy of central differential privacy with less than 16 bits of precision per value.
A scalable protocol for federated averaging with privacy and correctness guarantees.
problem Privacy and correctness in federated learning from multiple parties.
method Scalable protocol using correlated and independent Gaussian noise, analyzed for differential privacy and graph topology.
result Nearly matches trusted curator model's utility with minimal communication.
Paper studies optimal federated learning for nonparametric regression with privacy constraints.
problem Federated learning for nonparametric regression with heterogeneous differential privacy constraints.
method Proposes distributed privacy-preserving estimators and investigates their risk properties.
result Establishes matching minimax lower bounds for global and pointwise estimation.
Unified analysis of FL with arbitrary client participation.
problem Challenges of intermittent client availability and efficiency in FL.
method Introduces a generalized FedAvg and novel analysis capturing client participation.
result Unified convergence upper bounds for various participation patterns.
FedLCB-Q learns optimal policies from federated offline data with linear speedup.
problem Learning optimal policies from offline data with federated learning.
method Federated offline RL algorithm tailored for Q-learning, using local Q-function updates and central aggregation.
result Achieves linear speedup in sample complexity with collaboration among agents.
Federated Granger causality learns reliable interactions without sharing data.
problem Uncertainty in federated Granger causality estimates.
method Closed-form covariance recursions and spectral-radius-based convergence conditions.
result Uncertainty depends only on client data statistics and is independent of model parameters.
Our work specifies the fundamental cost of using secure aggregation in federated learning.
problem Training a distributed model with differential privacy constraints.
method Characterized the communication cost required for optimal accuracy under differential privacy, achieved by a linear scheme.
result The fundamental communication cost is $ ilde{O}\left( \min(n^2\varepsilon^2, d)
ight)$ bits per client, both sufficient and necessary.
New method tightens federated probe-logit distillation rates under varying bandwidths.
problem Estimating conditional distributions in federated learning with heterogeneous bandwidth constraints.
method Developed a new federated probe-logit distillation (FPLD) method with optimal allocation for varying bandwidths.
result Achieved matching lower and upper bounds for the minimax rate under heterogeneous bandwidths.
We study a recently proposed large-scale distributed learning paradigm, namely Federated Learning, where the worker machines are end users' own devices. Statistical and computational challenges arise in Federated Learning particularly in the presence of heterogeneous data distribution (i.e., data points on different de…
Securely analyzes survival data across multiple institutions without revealing individual patient records.
problem Privacy concerns in federated survival analysis of health data.
method Multiparty homomorphic encryption for approximate floating-point computation and encrypted aggregation.
result Privacy-preserving federated Kaplan--Meier survival analysis with high fidelity and predictable overhead.
A new algorithm reduces communication in graph-structured VFL.
problem Training GNNs in VFL with interrelated samples.
method Model splitting and lazy aggregation.
result GLASU trains GNNs with reduced communication, matching centralized performance.
Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learningstat.ML
New RL algorithms reduce costs for single-agent and federated learning.
problem Minimizing costs in RL and federated RL settings.
method Q-EarlySettled-LowCost and FedQ-EarlySettled-LowCost algorithms.
result First algorithms to achieve low burn-in and logarithmic switching costs.
SA-PEF improves federated learning efficiency by correcting gradient mismatches.
problem Slow decay of residual error in federated learning under non-IID data.
method Integrates step-ahead correction with partial error feedback.
result Achieves faster convergence and target accuracy compared to standard EF.
Safe-EF improves federated learning for non-smooth, constrained optimization.
problem Federated learning's communication bottlenecks with high-dimensional model updates.
method Error feedback (EF) for non-smooth convex optimization with safety constraints.
result Safe-EF matches lower complexity bounds and ensures safety constraints.
SPIRE enables efficient federated learning for diffusion models by separating client-specific embeddings from a shared backbone.
problem Large diffusion models are impractical for federated learning due to their size.
method SPIRE separates the network into a global backbone and client-specific embeddings, enabling efficient finetuning.
result SPIRE achieves parameter-efficient finetuning, updating only a small fraction of weights.
MARINA-P improves non-smooth federated optimization with adaptive stepsizes.
problem Non-smooth federated optimization in machine learning applications.
method Extends EF21-P and MARINA-P to non-smooth convex setting, proving optimal convergence rate and communication complexity bounds.
result MARINA-P achieves convergence rate and communication complexity matching classical subgradient methods.
FedIRT enables privacy-preserving psychometric estimation without centralizing data.
problem Privacy and data governance concerns in centralized IRT estimation.
method Federated Item Response Theory (FedIRT) and FedIRT-DP for differentially private estimation.
result FedIRT matches accuracy of centralized estimators while preserving privacy.
A federated learning algorithm tackles linear bandits with adversarial actions, achieving optimal regret bounds.
problem Federated linear bandits with finite adversarial action sets.
method FedSupLinUCB algorithm, extending SupLinUCB and OFUL principles.
result Achieves a total regret of , matching minimax lower bound and being order-optimal.
FedLoRU improves FL efficiency by using low-rank updates.
problem Communication inefficiency and performance reduction in Federated Learning.
method Proposes FedLoRU, a low-rank update framework for FL, which reduces communication costs while maintaining performance.
result FedLoRU achieves convergence rates similar to FedAvg and is robust to heterogeneous and large numbers of clients.
This paper analyzes error feedback in compressed federated learning for non-convex optimization problems.
problem Reducing communication cost in federated learning with biased gradient compression.
method Proposes Fed-EF, a compressed federated learning scheme with error feedback, and analyzes its convergence rate and performance under partial client participation.
result Fed-EF can match the convergence rate of full-precision FL under data heterogeneity with a linear speedup and no extra slow-down factor due to stale error compensation.
Compressed Federated Distillation reduces communication in federated learning.
problem Communication constraints in Federated Learning.
method Compressed Federated Distillation (CFD) leverages soft labels and quantization techniques.
result Reduces communication by more than 4 orders of magnitude compared to Federated Averaging.
Paper proposes SCALLION and SCAFCOM for compressed FL with reduced communication.
problem Reducing communication overhead in Federated Learning with data heterogeneity and partial participation.
method Revisit and simplify stochastic controlled averaging, proposing SCALLION and SCAFCOM for unbiased and biased compression.
result SCALLION and SCAFCOM outperform existing methods in communication and computation complexities.
This paper improves model fusion by training-time neuron alignment, reducing barriers in multi-model fusion.
problem Diverse neuron permutations across different settings hinder model fusion performances.
method Training-time neuron alignment using fixed neuron anchors to reduce training-time permutations.
result Training-time neuron alignment improves fusion of pretrained models and federated learning performances.
This paper develops a federated EM algorithm for unsupervised learning of mixture models.
problem Theoretical foundations of unsupervised federated learning are lacking.
method Introduces a federated gradient EM algorithm (FedGrEM) for unsupervised learning of mixture models.
result Theoretical analysis shows FedGrEM outperforms local single-task learning.