| Venue | 2024-2020 | before 2020 | | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | | IJCAI | 25, 24, 23, 22, 21, 20 | 19 | | AAAI | 26, 25, 24, 23, 22, 21, 20 | - | | AISTATS | 25, 24, 23, 22, 21, 20 | - | | ALT | 22 | - | | AI (J) | 26, 25, 23 | - | | NeurIPS | 24, 23, 22, 21, 20 | 18, 17 | | ICML | 25, 24, 23, 22, 21, 20 | 19 | | ICLR | 25, 24, 23, 22, 21, 20 | - | | COLT | 23 | - | | UAI | 25, 24, 23, 22, 21 | - | | Machine Learning (J) | 26, 25, 24, 23, 22 | - | | JMLR (J) | 25, 24, 23, 22 | - | | TPAMI (J) | 26, 25, 24, 23, 22 | - | | KDD | 26, 25, 24, 23, 22, 21, 20 | | | WSDM | 26,25, 24, 23, 22, 21 | 19 | | S&P | 25, 24, 23, 22 | 19 | | CCS | 25, 24, 23, 22, 21, 19 | 17 | | USENIX Security | 25, 24, 23, 22, 20 | - | | NDSS | 26, 25, 24, 23, 22, 21 | - | | CVPR | 25, 24, 23, 22, 21 | - | | ICCV | 23,21 | - | | ECCV | 24, 22, 20 | - | | MM | 25, 24, 23, 22, 21, 20 | - | | IJCV (J) | 25, 24 | - | | ACL | 25, 24, 23, 22, 21 | 19 | | NAACL | 24, 22, 21 | - | | EMNLP | 25, 24, 23, 22, 21, 20 | - | | COLING | 25, 20 | - | | SIGIR | 25, 24, 23, 22, 21, 20 | - | | SIGMOD | 25, 24, 23, 22, 21 | - | | ICDE | 25, 24, 23, 22, 21 | - | | VLDB | 25, 24, 23, 22, 21, 21, 20 | - | | SIGCOMM | 25 | - | | INFOCOM | 25, 24, 23, 22, 21, 20 | 19, 18 | | MobiCom | 25, 24, 23, 22, 21, 20 | | | NSDI | 25, 23(1, 2) | - | | WWW | 26, 25, 24, 23, 22, 21 | | | OSDI | 21 | - | | SOSP | 21 | - | | ISCA | 24 | - | | MLSys | 25, 24, 23, 22, 20 | 19 | | EuroSys | 26, 25, 24, 23, 22, 21, 20 | | | TPDS (J) | 26, 25, 24, 23, 22, 21, 20 | - | | DAC | 25, 24, 22, 21 | - | | TOCS | - | - | | TOS | - | - | | TCAD | 26, 25, 24, 23, 22, 21 | - | | TC | 26, 25, 24, 23, 22, 21 | - | | ICSE | 25, 23, 21 | - | | FOCS | - | - | | STOC | - | - |

|Title | Venue | Year | Materials| | ------------------------------------------------------------ | --------------------- | ---- | ------------------------------------------------------------ | | Towards compute-efficient Byzantine-robust federated learning with fully homomorphic encryption | Nat. Mach. Intell. | 2025 | [PUB] [PDF] [CODE] | | Incentivizing inclusive contributions in model sharing markets | Nat. Commun. | 2025 | [PUB] [CODE] | | FedECA: federated external control arms for causal inference with time-to-event data in distributed settings | Nat. Commun. | 2025 | [PUB] [CODE] | | Privacy-preserving multicenter differential protein abundance analysis with FedProt | Nat. Comput. Sci. | 2025 | [PUB] [CODE] | | Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge | Nat. Commun. | 2025 | [PUB] [CODE] | | A fully open AI foundation model applied to chest radiography | Nature | 2025 | [PUB] [CODE] | | Federated learning using a memristor compute-in-memory chip with in situ physical unclonable function and true random number generator | Nat. Electron. | 2025 | [PUB] | | A framework reforming personalized Internet of Things by federated meta-learning | Nat. Commun. | 2025 | [PUB] [CODE] | | Achieving flexible fairness metrics in federated medical imaging | Nat. Commun. | 2025 | [PUB] [CODE] | | Towards fairness-aware and privacy-preserving enhanced collaborative learning for healthcare | Nat. Commun. | 2025 | [PUB] [CODE] | | Data-driven federated learning in drug discovery with knowledge distillation | Nat. Mach. Intell. | 2025 | [PUB] [CODE] | | Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals | Nat. Commun. | 2025 | [PUB] | | Physical unclonable in-memory computing for simultaneous protecting private data and deep learning models | Nat. Commun. | 2025 | [PUB] [新闻] | | MatSwarm: trusted swarm transfer learning driven materials computation for secure big data sharing | Nat. Commun. | 2024 | [PUB] [CODE] | | Introducing edge intelligence to smart meters via federated split learning | Nat. Commun. | 2024 | [PUB] [新闻] | | An international study presenting a federated learning AI platform for pediatric brain tumors | Nat. Commun. | 2024 | [PUB] [CODE] | | PPML-Omics: A privacy-preserving federated machine learning method protects patients’ privacy in omic data | Science Advances | 2024 | [PUB] [CODE] | | Federated learning is not a cure-all for data ethics | Nat. Mach. Intell.(Comment) | 2024 | [PUB] | | Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence | Nat. Commun. | 2024 | [PUB] [CODE] | | Selective knowledge sharing for privacy-preserving federated distillation without a good teacher | Nat. Commun. | 2024 | [PUB] [PDF] [CODE] | | A federated learning system for precision oncology in Europe: DigiONE | Nat. Med. (Comment) | 2024 | [PUB] | | Multi-client distributed blind quantum computation with the Qline architecture | Nat. Commun. | 2023 | [PUB] [PDF] | | Device-independent quantum randomness–enhanced zero-knowledge proof | PNAS | 2023 | [PUB] [PDF] [新闻] | | Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning | Nat. Commun. | 2023 | [PUB] | | Advocating for neurodata privacy and neurotechnology regulation | Nat. Protoc. (Perspective) | 2023 | [PUB] | | Federated benchmarking of medical artificial intelligence with MedPerf | Nat. Mach. Intell. | 2023 | [PUB] [PDF] [CODE] | | Algorithmic fairness in artificial intelligence for medicine and healthcare | Nat. Biomed. Eng. (Perspective) | 2023 | [PUB] [PDF] | | Differentially private knowledge transfer for federated learning | Nat. Commun. | 2023 | [PUB] [CODE] | | Decentralized federated learning through proxy model sharing | Nat. Commun. | 2023 | [PUB] [PDF] [CODE] | | Federated machine learning in data-protection-compliant research | Nat. Mach. Intell.(Comment) | 2023 | [PUB] | | Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer | Nat. Med. | 2023 | [PUB] [CODE] | | Federated learning enables big data for rare cancer boundary detection | Nat. Commun. | 2022 | [PUB] [PDF] [CODE] | | Federated learning and Indigenous genomic data sovereignty | Nat. Mach. Intell. (Comment) | 2022 | [PUB] | | Federated disentangled representation learning for unsupervised brain anomaly detection | Nat. Mach. Intell. | 2022 | [PUB] [PDF] [CODE] | | Shifting machine learning for healthcare from development to deployment and from models to data | Nat. Biomed. Eng. (Review Article) | 2022 | [PUB] | | A federated graph neural network framework for privacy-preserving personalization | Nat. Commun. | 2022 | [PUB] [CODE] [解读] | | Communication-efficient federated learning via knowledge distillation | Nat. Commun. | 2022 | [PUB] [PDF] [CODE] | | Lead federated neuromorphic learning for wireless edge artificial intelligence | Nat. Commun. | 2022 | [PUB] [CODE] [解读] | | A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data | Sci. Rep. | 2022 | [PUB] | | Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence | Nat. Mach. Intell. | 2021 | [PUB] [PDF] [CODE] | | Federated learning for predicting clinical outcomes in patients with COVID-19 | Nat. Med. | 2021 | [PUB] [CODE] | | Adversarial interference and its mitigations in privacy-preserving collaborative machine learning | Nat. Mach. Intell.(Perspective) | 2021 | [PUB] | | Swarm Learning for decentralized and confidential clinical machine learning :star: | Nature :mortar_board: | 2021 | [PUB] [CODE] [SOFTWARE] [解读] | | End-to-end privacy preserving deep learning on multi-institutional medical imaging | Nat. Mach. Intell. | 2021 | [PUB] [CODE] [解读] | | Communication-efficient federated learning | PANS. | 2021 | [PUB] [CODE] | | Breaking medical data sharing boundaries by using synthesized radiographs | Science. Advances. | 2020 | [PUB] [CODE] | | Secure, privacy-preserving and federated machine learning in medical imaging :star: | Nat. Mach. Intell.(Perspective) | 2020 | [PUB] |

2026

AAAI

• A Unified Self-Regulating Training Framework for Federated Deep Reinforcement Learning. [PUB]
• Bi-level Personalization for Federated Foundation Models: A Task-vector Aggregation Approach. [PUB]
• BIQ: Bisection Interval Quantization for Communication-efficient Federated Learning. [PUB]
• Breaking Cross-View Associations: Byzantine Model Poisoning Attack against Vertical Federated Learning. [PUB]
• Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach. [PUB]
• Causality-inspired Federated Learning for Dynamic Spatio-Temporal Graphs. [PUB]
• Causally-Aware Attribute Completion for Incomplete Federated Graph Clustering. [PUB]
• Class-Aware Active Annotation in Federated Semi-Supervised Learning for Medical Image Classification. [PUB]
• Communication-Efficient Heterogeneous Federated Learning with Sparse Prototypes in Resource-Constrained Environments. [PUB]
• CoRe-Fed: Bridging Collaborative and Representation Fairness via Federated Embedding Distillation. [PUB]
• DA-DFGAS: Differentiable Federated Graph Neural Architecture Search with Distribution-Aware Attentive Aggregation. [PUB]
• Data Heterogeneity and Forgotten Labels in Split Federated Learning. [PUB]
• Decoupling Shared and Personalized Knowledge: A Dual-Branch Federated Learning Framework for Multi-Domain with Non-IID Data. [PUB]
• Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Segmentation. [PUB]
• DoBlock: Blocking Malicious Association Propagation for Backdoor-Robust Federated Learning Under Domain Skew. [PUB]
• Domain-Aware Suppression and Aggregation for Federated DG ReID. [PUB]
• DSFedMed: Dual-Scale Federated Medical Image Segmentation via Mutual Distillation Between Foundation and Lightweight Models. [PUB]
• Enhanced Federated Deep Multi-View Clustering Under Uncertainty Scenario. [PUB]
• Equilibrium-Driven Vertical Federated Learning with Selective Privacy Protection. [PUB]
• EvoFMVC: Trusted Federated Multi-View Clustering with Evolutionary Fusion. [PUB]
• Feature-Aware One-Shot Federated Learning via Hierarchical Token Sequences. [PUB]
• FedAdamW: A Communication-Efficient Optimizer with Convergence and Generalization Guarantees for Federated Large Models. [PUB]
• FedALT: Federated Fine-Tuning Through Adaptive Local Training with Rest-of-World LoRA. [PUB]
• FedARKS: Federated Aggregation via Robust and Discriminative Knowledge Selection and Integration for Person Re-identification. [PUB]
• FedAU2: Attribute Unlearning for User-Level Federated Recommender Systems with Adaptive and Robust Adversarial Training. [PUB]
• FedBRICK: Structural Bias Aware Heterogeneous Foundation Model Federated Tuning. [PUB]
• FedCD: Towards Consolidated Distillation for Heterogeneous Federated Learning. [PUB]
• FedCure: Mitigating Participation Bias in Semi-Asynchronous Federated Learning with Non-IID Data. [PUB]
• FedDNA: DNA Sequence Reconstruction via Deep Evidential Learning and Personalized Federated Aggregation. [PUB]
• Federated CLIP for Resource-Efficient Heterogeneous Medical Image Classification. [PUB]
• Federated Context-Aware Personalized Recommendation. [PUB]
• Federated Graph-level Clustering Network with Attribute Inference. [PUB]
• Federated Incomplete Multi-View Clustering with Tensorized Low-Rank Constraint. [PUB]
• Federated Learning Playground. [PUB]
• Federated Linear Dueling Bandits. [PUB]
• Federated Vision-Language-Recommendation with Personalized Fusion. [PUB]
• FedLAGC: Towards High Performance System-Heterogeneous Federated Learning via Layer-Adaptive Submodel Extraction and Gradient Correction. [PUB]
• FedMerge: Federated Model Merging for Personalization. [PUB]
• FedPKDA: Personalized Federated Learning with Privacy-Preserving Knowledge Dynamic Alignment. [PUB]
• FedPM: Federated Learning Using Second-order Optimization with Preconditioned Mixing of Local Parameters. [PUB]
• FedP²EFT: Federated Learning to Personalize PEFT for Multilingual LLMs. [PUB]
• FedRNC: Addressing Spatio-Temporal Label Misalignment in Federated Noisy Class-Incremental Learning. [PUB]
• FedSDA: Federated Stain Distribution Alignment for Non-IID Histopathological Image Classification. [PUB]
• FedSDWC: Federated Synergistic Dual-Representation Weak Causal Learning for OOD. [PUB]
• FedSEA-LLaMA: A Secure, Efficient and Adaptive Federated Splitting Framework for Large Language Models. [PUB]
• FedShard: Federated Unlearning with Efficiency Fairness and Performance Fairness. [PUB]
• FedSkeleton: Secure Multi-Party Graph Skeleton Construction for Privacy-Preserving Federated Time-Series Forecasting. [PUB]
• FedTopo: Topology-Informed Representation Alignment in Federated Learning Under Non-I.I.D. Conditions. [PUB]
• FILTER: A Framework for Defending Against Backdoor Attacks in Vertical Federated Learning. [PUB]
• Generalizable Heterogeneity-aware Federated Feature and Basic-matrix Consistency Learning. [PUB]
• Generic Adversarial Attack Framework Against Graph-based Vertical Federated Learning. [PUB]
• Good Gradients Poison Your Model: Evading Defenses in Federated Learning via Boundary-adaptive Perturbation. [PUB]
• HealSplit: Towards Self-Healing Through Adversarial Distillation in Split Federated Learning. [PUB]
• Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulants. [PUB]
• Incomplete Multi-View Unsupervised Federated Feature Selection via Cooperative Particle Swarm Optimization and Tensor-Aligned Learning. [PUB]
• Inter-Client Dependency Recovery with Hidden Global Components for Federated Traffic Prediction. [PUB]
• Intra-Class Unbiased Prototype Aggregation and Classifier Collaboration for Personalized Federated Learning. [PUB]
• Investigating Social Bias Propagation in Federated Fine-tuning of Large Language Models. [PUB]
• LSHFed: Robust and Communication-Efficient Federated Learning with Locally-Sensitive Hashing Gradient Mapping. [PUB]
• MSCFL: Model Structure-Aware Clustered Federated Learning for System Heterogeneity and Data Drift. [PUB]
• Multi-Modal Style Transfer-based Prompt Tuning for Efficient Federated Domain Generalization. [PUB]
• MultiKD: Backdoor Defense in Federated Graph Learning via Attention-Guided Multi-Teacher Distillation. [PUB]
• Neuro-Symbolic Federated Learning over Heterogeneous Data-Views: A Structured Approach to Distributive EHR Modelling. [PUB]
• Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models. [PUB]
• Optimal Look-back Horizon for Time Series Forecasting in Federated Learning. [PUB]
• OPTION: An Online Pricing Strategy for Asynchronous Federated Learning Against Free-Riding Attacks. [PUB]
• OursFed: Provable Group Fairness-Aware Federated Learning Against Distrust and Fragility. [PUB]
• PAGE: A Unified Approach for Federated Graph Unlearning. [PUB]
• Personalized Federated Graph-Level Clustering Network. [PUB]
• Personalized Federated Learning with Bidirectional Communication Compression via One-Bit Random Sketching. [PUB]
• Plug-and-Play Parameter-Efficient Tuning of Embeddings for Federated Recommendation. [PUB]
• Poisoning with a Pill: Circumventing Detection in Federated Learning. [PUB]
• PPFL: A Parameter Behavior-Driven Plug-in Personalization Engine for Federated Learning. [PUB]
• Prior Refinement Is Better: Diffusion-Driven Graph Harmonization for Federated Graph Learning. [PUB]
• Re-architecting Personalized Federated Learning for Demanding Edge Environments. [PUB]
• REMISVFU: Vertical Federated Unlearning via Representation Misdirection for Intermediate Output Feature. [PUB]
• Retaliatory Attacks Against Federated Unlearning via Data Leakage. [PUB]
• Ripple Shapley: Data Influence Attribution in One Federated Training Run. [PUB]
• Scaling Law Analysis in Federated Learning: How to Select the Optimal Model Size?. [PUB]
• SFedHIFI: Fire Rate-Based Heterogeneous Information Fusion for Spiking Federated Learning. [PUB]
• ShadeEdit: A Utility-Preserving and Defense-Evasive Knowledge Manipulation Attack in Federated LLMs. [PUB]
• SMoFi: Step-wise Momentum Fusion for Split Federated Learning on Heterogeneous Data. [PUB]
• Tackling Resource-Constrained and Data-Heterogeneity in Federated Learning with Double-Weight Sparse Pack. [PUB]
• TOFA: Training-Free One-Shot Federated Adaptation for Vision-Language Models. [PUB]
• Topological Federated Clustering via Gravitational Potential Fields Under Local Differential Privacy. [PUB]
• Towards Federated Clustering: A Client-wise Private Graph Aggregation Framework. [PUB]
• Towards Robust Text-Attributed Federated Graph Learning: Multimodal Threats and Defense. [PUB]
• TransFR: Transferable Federated Recommendation with Adapter Tuning on Pre-trained Language Models. [PUB]
• Unlocking Dynamic Inter-Client Spatial Dependencies: A Federated Spatio-temporal Graph Learning Method for Traffic