- Personalized Federated Learning with Feature Alignment and Classifier Collaboration.
- MocoSFL: enabling cross-client collaborative self-supervised learning.
- Single-shot General Hyper-parameter Optimization for Federated Learning.
- Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated.
- FedExP: Speeding up Federated Averaging via Extrapolation.
- Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection.
- DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity.
- Machine Unlearning of Federated Clusters.
- Federated Neural Bandits.
- FedFA: Federated Feature Augmentation.
- Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach.
- Better Generative Replay for Continual Federated Learning.
- Federated Learning from Small Datasets.
- Federated Nearest Neighbor Machine Translation.
- Test-Time Robust Personalization for Federated Learning.
- DepthFL : Depthwise Federated Learning for Heterogeneous Clients.
- Towards Addressing Label Skews in One-Shot Federated Learning.
- Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning.
- Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation.
- SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication.
- Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses.
- Effective passive membership inference attacks in federated learning against overparameterized models.
- FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification.
- Multimodal Federated Learning via Contrastive Representation Ensemble.
- Faster federated optimization under second-order similarity.
- FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy.
- The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation.
- PerFedMask: Personalized Federated Learning with Optimized Masking Vectors.
- EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data.
- FedDAR: Federated Domain-Aware Representation Learning.
- Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning.
- FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning.
- Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses.
- Efficient Federated Domain Translation.
- On the Importance and Applicability of Pre-Training for Federated Learning.
- Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models.
- A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy.
- Instance-wise Batch Label Restoration via Gradients in Federated Learning.
- Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity.
- Meta Knowledge Condensation for Federated Learning.
- CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning.
- Sparse Random Networks for Communication-Efficient Federated Learning.
- Combating Exacerbated Heterogeneity for Robust Decentralized Models.
- Hyperparameter Optimization through Neural Network Partitioning.
- Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision?
- Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top.
- Dual Diffusion Implicit Bridges for Image-to-Image Translation.
- Characterizing Internal Evasion Attacks in Federated Learning.
- Byzantine-Robust Federated Learning with Optimal Statistical Rates.
- The communication cost of security and privacy in federated frequency estimation.
- Federated Averaging Langevin Dynamics: Toward a unified theory of and new algorithms.
- Active Membership Inference Attack under Local Differential Privacy in Federated Learning.
- Private Non-Convex Federated Learning Without a Trusted Server.
- Federated Learning under Distributed Concept Drift.
- Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout.
- Nothing but Regrets --- Privacy-Preserving Federated Causal Discovery.
- Federated Learning for Data Streams.
- Federated Asymptotics: a model to compare federated learning algorithms.
- FedTree: A Federated Learning System For Trees.
- GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning.
- FLINT: A Platform for Federated Learning Integration.
- On Noisy Evaluation in Federated Hyperparameter Tuning.
- Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding.
- AgrEvader: Poisoning Membership Inference Against Byzantine-robust Federated Learning.
- Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning.
- Semi-decentralized Federated Ego Graph Learning for Recommendation.
- Federated Node Classification over Graphs with Latent Link-type Heterogeneity.
- FedEdge: Accelerating Edge-Assisted Federated Learning.
- FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures.
- To Store or Not? Online Data Selection for Federated Learning with Limited Storage.
- Interaction-level Membership Inference Attack Against Federated Recommender Systems.
- FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection.
- pFedPrompt: Learning Personalized Prompt for Vision-Language Models in Federated Learning.
- SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost.
- Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors.
- Label Inference Attacks Against Vertical Federated Learning.
- FLAME: Taming Backdoors in Federated Learning.
- SNARKBlock: Federated Anonymous Blocklisting from Hidden Common Input Aggregate Proofs.
- Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning.
- Enhancing Federated Learning with In-Cloud Unlabeled Data.
- FedMP: Federated Learning through Adaptive Model Pruning in Heterogeneous Edge Computing.
- Efficient Participant Contribution Evaluation for Horizontal and Vertical Federated Learning.
- Federated Learning on Non-IID Data Silos: An Experimental Study.
- Enhancing Federated Learning with Intelligent Model Migration in Heterogeneous Edge Computing.
- Improving Fairness for Data Valuation in Horizontal Federated Learning.
- FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity.
- FedRecAttack: Model Poisoning Attack to Federated Recommendation.
- Samba: A System for Secure Federated Multi-Armed Bandits.
- Federated Boosted Decision Trees with Differential Privacy.
- CERBERUS: Exploring Federated Prediction of Security Events.
- Eluding Secure Aggregation in Federated Learning via Model Inconsistency.
- EIFFeL: Ensuring Integrity for Federated Learning.
- Local and Central Differential Privacy for Robustness and Privacy in Federated Learning.
- DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection.
- FedCRI: Federated Mobile Cyber-Risk Intelligence.
- Interpretable Federated Transformer Log Learning for Cloud Threat Forensics.
- LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning.
- PAPAYA: Practical, Private, and Scalable Federated Learning.
- Practical Lossless Federated Singular Vector Decomposition over Billion-Scale Data.
- FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks.
- Collaboration Equilibrium in Federated Learning.
- Connecting Low-Loss Subspace for Personalized Federated Learning.
- Communication-Efficient Robust Federated Learning with Noisy Labels.
- FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy.
- FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients.
- No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices.
- Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to Dispatch.
- FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning.
- FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling.
- Felicitas: Federated Learning in Distributed Cross Device Collaborative Frameworks.
- A Practical Introduction to Federated Learning.
- Federated Unlearning via Class-Discriminative Pruning.
- An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning.
- FedKC: Federated Knowledge Composition for Multilingual Natural Language Understanding.
- PyramidFL: a fine-grained client selection framework for efficient federated learning.
- FedHD: federated learning with hyperdimensional computing.
- NestFL: efficient federated learning through progressive model pruning in heterogeneous edge computing.
- Federated learning-based air quality prediction for smart cities using BGRU model.
- FedScale: Benchmarking Model and System Performance of Federated Learning.
- Redundancy in cost functions for Byzantine fault-tolerant federated learning.
- Towards an Efficient System for Differentially-private, Cross-device Federated Learning.
- GradSec: a TEE-based Scheme Against Federated Learning Inference Attacks.
- Community-Structured Decentralized Learning for Resilient EI.
- Separation of Powers in Federated Learning (Poster Paper).
- HADFL: Heterogeneity-aware Decentralized Federated Learning Framework.
- FedLight: Federated Reinforcement Learning for Autonomous Multi-Intersection Traffic Signal Control.
- Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration.
- POSEIDON: Privacy-Preserving Federated Neural Network Learning.
- FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping.
- Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning.