DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service.
AeroRec: An Efficient On-Device Recommendation Framework using Federated Self-Supervised Knowledge Distillation.
Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration.
BR-DeFedRL: Byzantine-Robust Decentralized Federated Reinforcement Learning with Fast Convergence and Communication Efficiency.
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning.
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes.
Titanic: Towards Production Federated Learning with Large Language Models.
FairFed: Improving Fairness and Efficiency of Contribution Evaluation in Federated Learning via Cooperative Shapley Value.
Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization.
Efficient and Straggler-Resistant Homomorphic Encryption for Heterogeneous Federated Learning.
Federated Analytics-Empowered Frequent Pattern Mining for Decentralized Web 3.0 Applications.
Federated Offline Policy Optimization with Dual Regularization.
FedTC: Enabling Communication-Efficient Federated Learning via Transform Coding.
Heroes: Lightweight Federated Learning with Neural Composition and Adaptive Local Update in Heterogeneous Edge Networks.
A Semi-Asynchronous Decentralized Federated Learning Framework via Tree-Graph Blockchain.
Momentum-Based Federated Reinforcement Learning with Interaction and Communication Efficiency.
SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation.
Strategic Data Revocation in Federated Unlearning.
Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression.
Tomtit: Hierarchical Federated Fine-Tuning of Giant Models based on Autonomous Synchronization.
GraphProxy: Communication-Efficient Federated Graph Learning with Adaptive Proxy.
Towards Efficient Asynchronous Federated Learning in Heterogeneous Edge Environments.
Tackling System Induced Bias in Federated Learning: Stratification and Convergence Analysis.
Federated Learning under Heterogeneous and Correlated Client Availability.
Network Adaptive Federated Learning: Congestion and Lossy Compression.
FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning.
More than Enough is Too Much: Adaptive Defenses against Gradient Leakage in Production Federated Learning.
Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling.
A Reinforcement Learning Approach for Minimizing Job Completion Time in Clustered Federated Learning.
Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning.
Privacy as a Resource in Differentially Private Federated Learning.
A Hierarchical Knowledge Transfer Framework for Heterogeneous Federated Learning.
FedMoS: Taming Client Drift in Federated Learning with Double Momentum and Adaptive Selection.
Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization.
Oblivion: Poisoning Federated Learning by Inducing Catastrophic Forgetting.
AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation.
Toward Sustainable AI: Federated Learning Demand Response in Cloud-Edge Systems via Auctions.
AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices.
Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks.
SplitGP: Achieving Both Generalization and Personalization in Federated Learning.
Heterogeneity-Aware Federated Learning with Adaptive Client Selection and Gradient Compression.
Enabling Communication-Efficient Federated Learning via Distributed Compressed Sensing.
SVDFed: Enabling Communication-Efficient Federated Learning via Singular-Value-Decomposition.
Joint Edge Aggregation and Association for Cost-Efficient Multi-Cell Federated Learning.
TVFL: Tunable Vertical Federated Learning towards Communication-Efficient Model Serving.
Federated Learning with Flexible Control.
Asynchronous Federated Unlearning.
Joint Participation Incentive and Network Pricing Design for Federated Learning.
FedFPM: A Unified Federated Analytics Framework for Collaborative Frequent Pattern Mining.
Protect Privacy from Gradient Leakage Attack in Federated Learning.
A Profit-Maximizing Model Marketplace with Differentially Private Federated Learning.
Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization.
Optimal Rate Adaption in Federated Learning with Compressed Communications.
Towards Optimal Multi-Modal Federated Learning on Non-IID Data with Hierarchical Gradient Blending.
FLASH: Federated Learning for Automated Selection of High-band mmWave Sectors.
Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks.
Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling.
The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining.
Cost-Effective Federated Learning Design.
An Incentive Mechanism for Cross-Silo Federated Learning: A Public Goods Perspective.
Sample-level Data Selection for Federated Learning.
Learning for Learning: Predictive Online Control of Federated Learning with Edge Provisioning.
FAIR: Quality-Aware Federated Learning with Precise User Incentive and Model Aggregation.
Resource-Efficient Federated Learning with Hierarchical Aggregation in Edge Computing.
FedSens: A Federated Learning Approach for Smart Health Sensing with Class Imbalance in Resource Constrained Edge Computing.
FedServing: A Federated Prediction Serving Framework Based on Incentive Mechanism.
Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach.
To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices.
P-FedAvg: Parallelizing Federated Learning with Theoretical Guarantees.
Dual Attention-Based Federated Learning for Wireless Traffic Prediction.
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation.
Physical-Layer Arithmetic for Federated Learning in Uplink MU-MIMO Enabled Wireless Networks.
Optimizing Federated Learning on Non-IID Data with Reinforcement Learning.
Enabling Execution Assurance of Federated Learning at Untrusted Participants.
Last updated 6 months ago