On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

Trend · papers per month

count % of corpus

48 results for “Federated Reinforcement Learning”

Local Stochastic Approximation: A Unified View of Federated Learning and Distributed Multi-Task Reinforcement Learning Algorithmscs.LG

Unified view of federated learning and distributed RL using local stochastic approximation.

problem Finding the root of an operator composed of local operators in a network of agents with dependent data.

method Local stochastic approximation over a network of agents with Markov process-dependent data.

result Convergence rates of local stochastic approximation for both constant and time-varying step sizes, within a logarithmic factor of independent data.

federated learning distributed RL local stochastic approximation Markov processes convergence rates

2020-06-24abs ↗pdf ↗

Federated Reinforcement Learning with Environment Heterogeneitycs.LG

Two algorithms improve Federated RL in diverse environments.

problem Collaborative learning in environments with varying dynamics.

method Proposed two federated RL algorithms, QAvg and PAvg, and a personalization heuristic.

result Achieved better performance and generalization in diverse environments.

Federated Reinforcement Learning Environment Heterogeneity Personalization Value Function Policy Function

2022-04-06abs ↗pdf ↗

Federated Reinforcement Learning with Constraint Heterogeneitycs.LG

Study Federated RL with diverse constraints, proposing new optimization methods.

problem Solving reinforcement learning with multiple constraints in federated learning.

method Federated primal-dual policy optimization methods based on policy gradient methods.

result FedNPG achieves global convergence with an

ilde{O}(1/\sqrt{T})

rate.

Federated Learning Reinforcement Learning Constraint Heterogeneity Policy Optimization Global Convergence

2024-05-06abs ↗pdf ↗

Federated Offline Reinforcement Learningstat.ML

Paper proposes federated offline RL for personalized medicine.

problem Privacy constraints and heterogeneity in healthcare data.

method Multi-site Markov decision process model and first federated policy optimization algorithm.

result The proposed algorithm achieves comparable suboptimality to centralized RL.

federated learning reinforcement learning personalized medicine offline RL privacy constraints

2022-06-11abs ↗pdf ↗

Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Coststat.ML

Paper proposes FedQ-Advantage for federated Q-learning with near-optimal regret and low communication cost.

problem Near-optimal federated Q-learning with low communication cost.

method Reference-advantage decomposition for variance reduction, synchronization between agents and server, policy update.

result Achieves almost optimal regret and near-linear regret speedup compared to single-agent learning.

federated learning Q-learning reinforcement learning regret minimization variance reduction

2024-05-29abs ↗pdf ↗

Federated Q-Learning: Linear Regret Speedup with Low Communication Costcs.LG

Federated Q-Learning achieves linear regret speedup with low communication cost.

problem Achieving linear regret speedup in federated reinforcement learning without high communication costs.

method Proposed two federated Q-Learning algorithms: FedQ-Hoeffding and FedQ-Bernstein, using event-triggered synchronization, novel step size selection, and concentration inequalities.

result Total regrets achieve linear speedup compared to single-agent counterparts with logarithmic communication cost.

federated learning reinforcement learning linear regret model-free communication cost

2023-12-22abs ↗pdf ↗

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

ilde{\mathcal{O}}(\sqrt{H^3 |\mathcal{S}| |\mathcal{A}| T / M})

with small additional term due to heterogeneity.

Federated Learning Reinforcement Learning UCBVI Heterogeneity Communication Efficiency

2024-10-30abs ↗pdf ↗

Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Sufficescs.LG

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 Learning Offline Reinforcement Learning Q-Learning Collaborative Learning Sample Complexity

2024-02-08abs ↗pdf ↗

The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyondcs.LG

Federated Q-learning achieves linear speedup with heterogeneity, improving sample complexity.

problem Collaborative learning in distributed RL settings with limited data sharing.

method Analyzes synchronous and asynchronous federated Q-learning, proposes importance averaging.

result Achieves linear speedup with heterogeneity, robust to local trajectory heterogeneity.

federated learning reinforcement learning Q-learning heterogeneity asynchronous

2023-05-18abs ↗pdf ↗

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.

Reinforcement Learning Federated Learning Cost Reduction Regret Minimization Burn-in Cost

2025-06-05abs ↗pdf ↗

Federated Reinforcement Distillation with Proxy Experience Memorycs.LG

In distributed reinforcement learning, it is common to exchange the experience memory of each agent and thereby collectively train their local models. The experience memory, however, contains all the preceding state observations and their corresponding policies of the host agent, which may violate the privacy of the ag…

2019-07-15abs ↗pdf ↗

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representationscs.LG

PFedRL-Rep learns shared and personalized policies for heterogeneous environments.

problem Poor performance of single policy in heterogeneous environments.

method Develops PFedRL-Rep framework with shared feature representation and personalized weights.

result Proves linear convergence speedup with respect to the number of agents.

Federated Reinforcement Learning Personalized Learning Shared Representations Temporal Difference Heterogeneous Environments

2024-11-22abs ↗pdf ↗

Sample Complexity of Average-Reward Q-Learning: From Single-agent to Federated Reinforcement Learningstat.ML

This work improves Q-learning for average-reward MDPs, reducing sample and communication complexities in federated settings.

problem Improving sample complexity of Q-learning for average-reward MDPs.

method Simple Q-learning algorithm with carefully chosen parameters for both single-agent and federated scenarios.

result Established first federated Q-learning algorithm for average-reward MDPs with provable efficiency in sample and communication complexities.

Q-learning average-reward federated learning sample complexity communication complexity

2026-01-20abs ↗pdf ↗

Federated Learning with Bayesian Differential Privacycs.LG

We consider the problem of reinforcing federated learning with formal privacy guarantees. We propose to employ Bayesian differential privacy, a relaxation of differential privacy for similarly distributed data, to provide sharper privacy loss bounds. We adapt the Bayesian privacy accounting method to the federated sett…

2019-11-22abs ↗pdf ↗

Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approachcs.LG

A DRL approach optimizes resource allocation in BFL to reduce latency and energy consumption.

problem Energy and CPU constraints of mobile devices and increased training latency due to blockchain mining.

method Deep Reinforcement Learning (DRL) to derive optimal decisions for MLMO.

result Optimal resource allocation and block generation rate to minimize system latency, energy consumption, and incentive cost.

Blockchain Federated Learning Deep Reinforcement Learning Resource Management Mobile Devices

2020-04-08abs ↗pdf ↗

Safe-EF: Error Feedback for Nonsmooth Constrained Optimizationcs.LG

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.

Federated Learning Error Feedback Non-smooth Optimization Safety Constraints Contractive Compression

2025-05-09abs ↗pdf ↗

Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networkscs.IT

A novel method for efficient CDRL over wireless networks.

problem Challenges in collaborative deep reinforcement learning over wireless networks.

method Semantic-aware heterogeneous federated deep reinforcement learning (HFDRL) algorithm.

result Superior performance compared to state-of-the-art baselines.

semantic-aware collaborative deep reinforcement learning wireless networks federated learning deep reinforcement learning

2021-11-23abs ↗pdf ↗

A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detectionq-fin.CP

Improved fraud detection in finance with quantum-enhanced federated learning.

problem Challenges in detecting financial fraud with traditional methods.

method Hybrid quantum-enhanced federated learning framework combining quantum LSTM with privacy-preserving techniques.

result Approximately 5% improvement in performance metrics compared to conventional models.

fraud detection federated learning quantum computing privacy-preserving financial security

2025-07-15abs ↗pdf ↗

Robust Federated Learning Through Representation Matching and Adaptive Hyper-parameterscs.LG

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-…

2019-12-30abs ↗pdf ↗

An Energy and Carbon Footprint Analysis of Distributed and Federated Learningcs.LG

This paper analyzes energy and carbon footprints in distributed and federated learning.

problem High energy costs and carbon emissions in centralized AI methods.

method A novel framework quantifying energy and carbon footprints in vanilla and consensus-based FL methods.

result Optimal bounds and operational points for green FL designs and sustainability assessment.

distributed learning federated learning energy efficiency carbon footprint sustainability

2022-06-21abs ↗pdf ↗

HEX: Human-in-the-loop Explainability via Deep Reinforcement Learningcs.LG

Proposes HEX for human-in-the-loop explainability in ML models.

problem Ensuring accountability and reliability in ML models used for high-stakes decisions.

method Human-in-the-loop deep reinforcement learning approach to MLX.

result Synthesizes decision-specific policies from any classification model.

explainability machine learning deep reinforcement learning human-in-the-loop accountability

2022-06-02abs ↗pdf ↗

Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximationcs.LG

The paper tackles cooperative RL with function approximation, achieving near-optimal learning with limited communication.

problem Cooperative multi-agent reinforcement learning with function approximation.

method Careful message-passing and cooperative value iteration.

result Achieving near-optimal no-regret learning with limited communication in cooperative multi-agent settings.

reinforcement learning multi-agent systems cooperative learning function approximation no-regret

2021-03-08abs ↗pdf ↗

Federated Learning for Feature Generalization with Convex Constraintscs.LG

FedCONST adapts update magnitudes to enhance feature generalization in FL.

problem Heterogeneous client data in FL leads to overfitting and distorted transferable features.

method FedCONST uses linear convex constraints to stabilize training and preserve generalization.

result FedCONST enhances feature transferability and robustness, achieving state-of-the-art performance.

FedCONST FL generalization feature transfer convex constraints

2026-06-12abs ↗pdf ↗

Differentially Private Meta-Learningcs.LG

Parameter-transfer is a well-known and versatile approach for meta-learning, with applications including few-shot learning, federated learning, and reinforcement learning. However, parameter-transfer algorithms often require sharing models that have been trained on the samples from specific tasks, thus leaving the task…

2019-09-12abs ↗pdf ↗

Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithmsstat.ML

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.

federated learning unsupervised learning federated EM mixture models finite-sample theory

2023-10-23abs ↗pdf ↗

Practical One-Shot Federated Learning for Cross-Silo Settingcs.LG

A practical one-shot federated learning algorithm for cross-silo setting.

problem Limited applicability of existing one-shot federated learning algorithms due to specific model support and lack of privacy guarantees.

method FedKT, a one-shot federated learning algorithm that supports any classification models and provides differential privacy guarantees.

result FedKT significantly outperforms other state-of-the-art federated learning algorithms with a single communication round.

federated learning one-shot cross-silo privacy differential privacy

2020-10-02abs ↗pdf ↗

The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systemscs.LG

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 learning benchmark suite federated machine learning throughput convergence time

2020-06-14abs ↗pdf ↗

Adaptive Federated Optimizationcs.LG

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.

federated learning adaptive optimization federated Averaging non-convex optimization client heterogeneity

2020-02-29abs ↗pdf ↗

A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncs.LG

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…

2019-07-23abs ↗pdf ↗

Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting Phasescs.LG

Although the challenge of the device connection is much relieved in 5G networks, the training latency is still an obstacle preventing Federated Learning (FL) from being largely adopted. One of the most fundamental problems that lead to large latency is the bad candidate-selection for FL. In the dynamic environment, the…

2020-02-03abs ↗pdf ↗

Communication-Efficient Federated Distillationcs.LG

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.

Federated Learning Communication Efficiency Federated Distillation Soft Labels Quantization

2020-12-01abs ↗pdf ↗

Federated

f

-Differential Privacystat.ML

New federated $f$ -differential privacy for collaborative learning.

problem Privacy in federated learning.

method Introducing federated

f

-differential privacy and proposing a generic private federated learning framework.

result Proves federated

f

-differential privacy provides privacy guarantee on each record of one client's data.

federated learning privacy differential privacy f-differential privacy collaborative learning

2021-02-22abs ↗pdf ↗

Randomized Exploration in Cooperative Multi-Agent Reinforcement Learningcs.LG

Unified framework for randomized exploration in cooperative MARL.

problem Efficient exploration in cooperative multi-agent reinforcement learning.

method Unified algorithm framework with two Thompson Sampling algorithms, CoopTS-PHE and CoopTS-LMC.

result Theoretical

\widetilde{\mathcal{O}}(d^{3/2}H^2\sqrt{MK})

regret bound for parallel MDPs with linear transition.

Cooperative MARL Randomized Exploration Thompson Sampling Regret Bound Federated Learning

2024-04-16abs ↗pdf ↗

pFedGame -- Decentralized Federated Learning using Game Theory in Dynamic Topologystat.ML

pFedGame uses game theory for decentralized federated learning in dynamic networks.

problem Performance bottlenecks, data bias, model convergence issues, and model poisoning attacks in federated learning.

method pFedGame employs game theory to decentralize federated learning, avoiding a central aggregation server and addressing dynamic network challenges.

result pFedGame achieves higher accuracy (over 70%) in heterogeneous data compared to existing methods.

federated learning decentralized game theory dynamic networks model convergence

2024-10-05abs ↗pdf ↗

Federated Asymptotics: a model to compare federated learning algorithmscs.LG

A framework to compare federated learning algorithms in high-dimensional settings.

problem Comparing the performance of federated learning algorithms in high-dimensional settings.

method Formulating federated learning as a multi-criterion objective and analyzing a linear regression model.

result Federated Averaging with simple client fine-tuning achieves the same asymptotic risk as more intricate approaches and outperforms without personalization.

federated learning asymptotics personalization information sharing meta-learning

2021-08-16abs ↗pdf ↗

Federated Learning as a Mean-Field Gamestat.ML

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.

federated learning mean-field games machine learning game theory control theory

2021-07-08abs ↗pdf ↗

Hierarchically Fair Federated Learningcs.LG

A fair reward system boosts participation in federated learning.

problem Fairness in federated learning among competitive agents with siloed data.

method Hierarchically fair federated learning (HFFL) framework with proportional rewards based on contribution levels.

result Efficacy of HFFL in maintaining fairness and facilitating federated learning in competitive settings.

federated learning fairness competitive agents hierarchical reward proportional rewards

2020-04-22abs ↗pdf ↗

Federated Multi-Mini-Batch: An Efficient Training Approach to Federated Learning in Non-IID Environmentscs.LG

Federated multi-mini-batch improves efficiency in non-IID environments.

problem Performance and communication efficiency challenges in federated learning with non-IID data.

method Introduces federated multi-mini-batch approach to balance performance and communication.

result Federated multi-mini-batch outperforms federated averaging in non-IID settings.

Federated Learning Non-IID Data Mini-Batch Communication Efficiency

2020-11-13abs ↗pdf ↗

Towards Fair Federated Learning with Zero-Shot Data Augmentationcs.CV

This work improves fairness in federated learning by using zero-shot data augmentation.

problem Statistical heterogeneity leads to biased and less uniform accuracy across clients in federated learning.

method Proposes a federated learning system with zero-shot data augmentation to mitigate statistical heterogeneity and improve fairness.

result Empirical results show improved test accuracy and fairness across clients.

federated learning zero-shot data augmentation statistical heterogeneity fairness accuracy

2021-04-27abs ↗pdf ↗

Variational Federated Multi-Task Learningcs.LG

In federated learning, a central server coordinates the training of a single model on a massively distributed network of devices. This setting can be naturally extended to a multi-task learning framework, to handle real-world federated datasets that typically show strong statistical heterogeneity among devices. Despite…

2019-06-14abs ↗pdf ↗

Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacycs.LG

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 differential privacy Sherpa.ai edge AI privacy protection

2020-07-02abs ↗pdf ↗

What Do We Mean by Generalization in Federated Learning?cs.LG

Federated learning studies separate client data and distribution gaps.

problem Understanding performance differences in federated learning across different datasets.

method Proposed a framework to disentangle out-of-sample and participation gaps.

result Dataset synthesis strategy is crucial for realistic simulations of federated learning generalization.

Federated Learning Generalization Client Data Distribution Gaps Synthetic Data

2021-10-27abs ↗pdf ↗

Dynamic Defense Against Byzantine Poisoning Attacks in Federated Learningcs.LG

Dynamic defense against Byzantine poisoning in federated learning.

problem Byzantine poisoning attacks on federated learning models.

method Dynamic federated aggregation operator to filter out adversarial clients.

result Dynamic selection of clients improves global learning model performance.

federated learning byzantine poisoning aggregation operator adversarial attacks deep learning

2020-07-29abs ↗pdf ↗

Communication-Efficient Adaptive Federated Learningcs.LG

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.

federated learning communication efficiency adaptive optimization federated adaptive convergence analysis

2022-05-05abs ↗pdf ↗

Pretraining Federated Text Models for Next Word Predictioncs.LG

Research improves federated text models for next word prediction.

problem Training models on distributed devices efficiently and effectively.

method Employ transfer learning in federated learning for next word prediction.

result Enhancements to current baselines with pretrained embeddings and whole model pretraining.

federated learning next word prediction transfer learning word embeddings model pretraining

2020-05-11abs ↗pdf ↗

Federated Learning with Communication Delay in Edge Networkscs.LG

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.

federated learning communication delays edge networks federated averaging convergence speed

2020-08-21abs ↗pdf ↗

Federated Covariate Shift Adaptation for Missing Target Output Valuesstat.ML

Federated learning method improves covariate shift adaptation for missing target values.

problem Missing target values in federated learning.

method Federated covariate shift adaptation algorithm for missing target output values.

result Asymptotically unbiased and efficient algorithm for federated learning.

federated learning covariate shift missing target asymptotic variance

2023-02-28abs ↗pdf ↗

Privacy Threats Against Federated Matrix Factorizationcs.CR

This paper analyzes privacy threats in federated matrix factorization.

problem Privacy threats in federated matrix factorization models.

method Categorizes federated matrix factorization into three types and analyzes privacy threats.

result This is the first study of privacy threats in federated matrix factorization.

federated learning matrix factorization privacy threats privacy-preserving approaches federated matrix factorization types

2020-07-03abs ↗pdf ↗