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@@ -195,26 +195,26 @@ Federated Learning papers accepted by top AI(Artificial Intelligence) conference
 
 |Title                                                           |    Affiliation                                                     |    Venue      |    Year    |    Materials|
 | ------------------------------------------------------------ | ------------------------------------------------------------ | ------- | ---- | ------------------------------------------------------------ |
-|BOBA: Byzantine-Robust Federated Learning with Label Skewness | UIUC | AISTATS | 2024 | [PUB](https://proceedings.mlr.press/v238/bao24a.html) [PDF](https://arxiv.org/abs/2208.12932) [CODE](https://github.com/baowenxuan/BOBA) |
-|Federated Linear Contextual Bandits with Heterogeneous Clients | University of Virginia | AISTATS | 2024 | [PUB](https://proceedings.mlr.press/v238/blaser24a.html) [PDF](https://arxiv.org/abs/2403.00116) [CODE](https://github.com/blaserethan/HetoFedBandit) |
-|Federated Experiment Design under Distributed Differential Privacy | Stanford University; Meta | AISTATS | 2024 | [PUB](https://proceedings.mlr.press/v238/chen24c.html) [PDF](https://arxiv.org/abs/2311.04375) [CODE](https://drive.google.com/file/d/1ugYQQEIOwqc1oH8cUe6rf1mV91c-cF_g/view?usp=drive_link) |
-|Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression | Princeton University | AISTATS | 2024 | [PUB](https://proceedings.mlr.press/v238/chen24d.html) [PDF](https://arxiv.org/abs/2310.19059) CODE |
-|Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization | INRIA | AISTATS | 2024 | [PUB](https://proceedings.mlr.press/v238/even24a.html) [PDF](https://arxiv.org/abs/2311.00465) CODE |
-|SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization | INRIA | AISTATS | 2024 | [PUB](https://proceedings.mlr.press/v238/fraboni24a.html) [PDF](https://arxiv.org/abs/2211.11656) [CODE](https://github.com/Accenture/Labs-Federated-Learning/tree/SIFU) |
-|Compression with Exact Error Distribution for Federated Learning | École Polytechnique | AISTATS | 2024 | [PUB](https://proceedings.mlr.press/v238/hegazy24a.html) [PDF](https://arxiv.org/abs/2310.20682) [CODE](https://github.com/mahegz/CompWithExactError) |
-|Adaptive Federated Minimax Optimization with Lower Complexities | NJU; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence | AISTATS | 2024 | [PUB](https://proceedings.mlr.press/v238/huang24c.html) [PDF](https://arxiv.org/abs/2211.07303) |
-|Adaptive Compression in Federated Learning via Side Information | Stanford University; University of Padova | AISTATS | 2024 | [PUB](https://proceedings.mlr.press/v238/isik24a.html) [PDF](https://arxiv.org/abs/2306.12625) [CODE](https://github.com/FrancescoPase/Federated-KLMS) |
-|On-Demand Federated Learning for Arbitrary Target Class Distributions | UNIST | AISTATS | 2024 | [PUB](https://proceedings.mlr.press/v238/jeong24a.html) [CODE](https://github.com/eai-lab/On-DemandFL) |
-|FedFisher: Leveraging Fisher Information for One-Shot Federated Learning | CMU | AISTATS | 2024 | [PUB](https://proceedings.mlr.press/v238/jhunjhunwala24a.html) [PDF](https://arxiv.org/abs/2403.12329) [CODE](https://github.com/Divyansh03/FedFisher) |
-|Queuing dynamics of asynchronous Federated Learning | Huawei | AISTATS | 2024 | [PUB](https://proceedings.mlr.press/v238/leconte24a.html) [PDF](https://arxiv.org/abs/2405.00017) |
-|Personalized Federated X-armed Bandit | Purdue University | AISTATS | 2024 | [PUB](https://proceedings.mlr.press/v238/li24a.html) [PDF](https://arxiv.org/abs/2310.16323) [CODE](https://github.com/WilliamLwj/PyXAB) |
-|Federated Learning For Heterogeneous Electronic Health Records Utilising Augmented Temporal Graph Attention Networks | University of Oxford | AISTATS | 2024 | [PUB](https://proceedings.mlr.press/v238/molaei24a.html) [CODE](https://github.com/AnshThakur/FL4HeterogenousEHRs) |
-|Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization | University of Virginia | AISTATS | 2024 | [PUB](https://proceedings.mlr.press/v238/shen24c.html) [PDF](https://arxiv.org/abs/2311.00944) CODE |
-|Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters | Northwestern University | AISTATS | 2024 | [PUB](https://proceedings.mlr.press/v238/sun24a.html) [PDF](https://arxiv.org/abs/2306.03824) [CODE](https://github.com/fedcodexx/Generalization-of-Federated-Learning) |
-|Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains | Sofia University | AISTATS | 2024 | [PUB](https://proceedings.mlr.press/v238/tsoy24a.html) [PDF](https://arxiv.org/abs/2403.06672) [CODE](https://github.com/nikita-tsoy98/mutually-beneficial-federated-learning-replication) |
-|Analysis of Privacy Leakage in Federated Large Language Models | University of Florida | AISTATS | 2024 | [PUB](https://proceedings.mlr.press/v238/vu24a.html) [PDF](https://arxiv.org/abs/2403.04784) [CODE](https://github.com/vunhatminh/FL_Attacks.git) |
-|Invariant Aggregator for Defending against Federated Backdoor Attacks | UIUC | AISTATS | 2024 | [PUB](https://proceedings.mlr.press/v238/wang24e.html) [PDF](https://arxiv.org/abs/2210.01834) [CODE](https://github.com/Xiaoyang-Wang/InvariantAggregator) |
-|Communication-Efficient Federated Learning With Data and Client Heterogeneity | ISTA | AISTATS | 2024 | [PUB](https://proceedings.mlr.press/v238/zakerinia24a.html) [PDF](https://arxiv.org/abs/2206.10032) [CODE](https://github.com/ShayanTalaei/QuAFL) |
+| BOBA: Byzantine-Robust Federated Learning with Label Skewness | UIUC | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/bao24a.html)] [[PDF](https://arxiv.org/abs/2208.12932)] [[CODE](https://github.com/baowenxuan/BOBA)] |
+| Federated Linear Contextual Bandits with Heterogeneous Clients | University of Virginia | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/blaser24a.html)] [[PDF](https://arxiv.org/abs/2403.00116)] [[CODE](https://github.com/blaserethan/HetoFedBandit)] |
+| Federated Experiment Design under Distributed Differential Privacy | Stanford University; Meta | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/chen24c.html)] [[PDF](https://arxiv.org/abs/2311.04375)] [[CODE](https://drive.google.com/file/d/1ugYQQEIOwqc1oH8cUe6rf1mV91c-cF_g/view?usp=drive_link)] |
+| Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression | Princeton University | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/chen24d.html)] [[PDF](https://arxiv.org/abs/2310.19059)] |
+| Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization | INRIA | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/even24a.html)] [[PDF](https://arxiv.org/abs/2311.00465)] |
+| SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization | INRIA | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/fraboni24a.html)] [[PDF](https://arxiv.org/abs/2211.11656)] [[CODE](https://github.com/Accenture/Labs-Federated-Learning/tree/SIFU)] |
+| Compression with Exact Error Distribution for Federated Learning | École Polytechnique | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/hegazy24a.html)] [[PDF](https://arxiv.org/abs/2310.20682)] [[CODE](https://github.com/mahegz/CompWithExactError)] |
+| Adaptive Federated Minimax Optimization with Lower Complexities | NJU; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/huang24c.html)] [[PDF](https://arxiv.org/abs/2211.07303)] |
+| Adaptive Compression in Federated Learning via Side Information | Stanford University; University of Padova | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/isik24a.html)] [[PDF](https://arxiv.org/abs/2306.12625)] [[CODE](https://github.com/FrancescoPase/Federated-KLMS)] |
+| On-Demand Federated Learning for Arbitrary Target Class Distributions | UNIST | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/jeong24a.html)] [[CODE](https://github.com/eai-lab/On-DemandFL)] |
+| FedFisher: Leveraging Fisher Information for One-Shot Federated Learning | CMU | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/jhunjhunwala24a.html)] [[PDF](https://arxiv.org/abs/2403.12329)] [[CODE](https://github.com/Divyansh03/FedFisher)] |
+| Queuing dynamics of asynchronous Federated Learning | Huawei | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/leconte24a.html)] [[PDF](https://arxiv.org/abs/2405.00017)] |
+| Personalized Federated X-armed Bandit | Purdue University | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/li24a.html)] [[PDF](https://arxiv.org/abs/2310.16323)] [[CODE](https://github.com/WilliamLwj/PyXAB)] |
+| Federated Learning For Heterogeneous Electronic Health Records Utilising Augmented Temporal Graph Attention Networks | University of Oxford | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/molaei24a.html)] [[CODE](https://github.com/AnshThakur/FL4HeterogenousEHRs)] |
+| Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization | University of Virginia | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/shen24c.html)] [[PDF](https://arxiv.org/abs/2311.00944)] |
+| Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters | Northwestern University | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/sun24a.html)] [[PDF](https://arxiv.org/abs/2306.03824)] [[CODE](https://github.com/fedcodexx/Generalization-of-Federated-Learning)] |
+| Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains | Sofia University | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/tsoy24a.html)] [[PDF](https://arxiv.org/abs/2403.06672)] [[CODE](https://github.com/nikita-tsoy98/mutually-beneficial-federated-learning-replication)] |
+| Analysis of Privacy Leakage in Federated Large Language Models | University of Florida | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/vu24a.html)] [[PDF](https://arxiv.org/abs/2403.04784)] [[CODE](https://github.com/vunhatminh/FL_Attacks.git)] |
+| Invariant Aggregator for Defending against Federated Backdoor Attacks | UIUC | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/wang24e.html)] [[PDF](https://arxiv.org/abs/2210.01834)] [[CODE](https://github.com/Xiaoyang-Wang/InvariantAggregator)] |
+| Communication-Efficient Federated Learning With Data and Client Heterogeneity | ISTA | AISTATS | 2024 | [[PUB](https://proceedings.mlr.press/v238/zakerinia24a.html)] [[PDF](https://arxiv.org/abs/2206.10032)] [[CODE](https://github.com/ShayanTalaei/QuAFL)] |
 | FedMut: Generalized Federated Learning via Stochastic Mutation | NTU | AAAI | 2024 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29146)] |
 | Federated Partial Label Learning with Local-Adaptive Augmentation and Regularization | Carleton University | AAAI | 2024 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29562)] [[PAGE](https://underline.io/lecture/93915-federated-partial-label-learning-with-local-adaptive-augmentation-and-regularization)] |
 | No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation | IIT | AAAI | 2024 | [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28950)] [[PAGE](https://underline.io/lecture/93775-no-prejudice-fair-federated-graph-neural-networks-for-personalized-recommendation)] [[PDF](https://arxiv.org/abs/2312.10080)] [[CODE](https://github.com/anujksirohi/F2PGNN-AAAI24)] |
@@ -465,7 +465,7 @@ Federated Learning papers accepted by top ML(machine learning) conference and jo
 
 |Title                                                           |    Affiliation                                                     |    Venue             |    Year    |    Materials|
 | ------------------------------------------------------------ | ------------------------------------------------------------ | -------------- | ---- | ------------------------------------------------------------ |
-|Federated learning with superquantile aggregation for heterogeneous data. | Google Research | Mach Learn | 2024 | [PUB](https://link.springer.com/article/10.1007/s10994-023-06332-x) [PDF](https://arxiv.org/abs/2112.09429) [CODE](https://github.com/krishnap25/simplicial-fl) |
+| Federated learning with superquantile aggregation for heterogeneous data. | Google Research | Mach Learn | 2024 | [[PUB](https://link.springer.com/article/10.1007/s10994-023-06332-x)] [[PDF](https://arxiv.org/abs/2112.09429)] [[CODE](https://github.com/krishnap25/simplicial-fl)] |
 | Aligning model outputs for class imbalanced non-IID federated learning | NJU | Mach Learn | 2024 | [[PUB](https://link.springer.com/article/10.1007/s10994-022-06241-5)] |
 | The Impact of Adversarial Attacks on Federated Learning: A Survey | IIT | TPAMI | 2024 | [[PUB](https://ieeexplore.ieee.org/document/10274102)] |
 | Understanding and Mitigating Dimensional Collapse in Federated Learning | NUS | TPAMI | 2024 | [[PUB](https://ieeexplore.ieee.org/document/10336535)] [[PDF](https://arxiv.org/abs/2210.00226)] [[CODE](https://github.com/bytedance/FedDecorr)] |
@@ -1614,18 +1614,18 @@ Federated Learning papers accepted by top Database conference and journal, inclu
 
 |Title                                                           |    Affiliation                  |    Venue                        |    Year    |    Materials|
 | ------------------------------------------------------------ | ------------------------- | ------------------------- | ---- | ------------------------------------------------------------ |
-|DeTA: Minimizing Data Leaks in Federated Learning via Decentralized and Trustworthy Aggregation | IBM Research | EuroSys | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3627703.3650082) |
-|FLOAT: Federated Learning Optimizations with Automated Tuning | Virginia Tech | EuroSys | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3627703.3650081) [CODE](https://github.com/AFKD98/FLOAT/) |
-|Totoro: A Scalable Federated Learning Engine for the Edge | UCSC | EuroSys | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3627703.3629575) |
-|Dordis: Efficient Federated Learning with Dropout-Resilient Differential Privacy | HKUST | EuroSys | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3627703.3629559) [PDF](https://arxiv.org/abs/2209.12528) [CODE](https://github.com/samuelgong/dordis) |
-|FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN |  | EuroSys workshop | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3642968.3654813) |
-|ALS Algorithm for Robust and Communication-Efficient Federated Learning |  | EuroSys workshop | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3642970.3655842) |
-|FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission. |  | EuroSys workshop | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3642970.3655834) |
+| DeTA: Minimizing Data Leaks in Federated Learning via Decentralized and Trustworthy Aggregation | IBM Research | EuroSys | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3627703.3650082)] |
+| FLOAT: Federated Learning Optimizations with Automated Tuning | Virginia Tech | EuroSys | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3627703.3650081)] [[CODE](https://github.com/AFKD98/FLOAT/)] |
+| Totoro: A Scalable Federated Learning Engine for the Edge | UCSC | EuroSys | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3627703.3629575)] |
+| Dordis: Efficient Federated Learning with Dropout-Resilient Differential Privacy | HKUST | EuroSys | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3627703.3629559)] [[PDF](https://arxiv.org/abs/2209.12528)] [[CODE](https://github.com/samuelgong/dordis)] |
+| FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN |  | EuroSys workshop | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3642968.3654813)] |
+| ALS Algorithm for Robust and Communication-Efficient Federated Learning |  | EuroSys workshop | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3642970.3655842)] |
+| FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission. |  | EuroSys workshop | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3642970.3655834)] |
 | Collaboration in Federated Learning With Differential Privacy: A Stackelberg Game Analysis | SYSU | TPDS | 2024 | [[PUB](https://ieeexplore.ieee.org/document/10400897)] |
 | FAST: Enhancing Federated Learning Through Adaptive Data Sampling and Local Training | USTC | TPDS | 2024 | [[PUB](https://ieeexplore.ieee.org/document/10323207)] |
 | EcoFed: Efficient Communication for DNN Partitioning-Based Federated Learning | University of St Andrews | TPDS | 2024 | [[PUB](https://ieeexplore.ieee.org/document/10380682)] [[PDF](https://arxiv.org/abs/2304.05495)] [[CODE](https://github.com/blessonvar/ecofed)] |
 | FedHAP: Federated Hashing With Global Prototypes for Cross-Silo Retrieval | THU | TPDS | 2024 | [[PUB](https://ieeexplore.ieee.org/document/10286313)] [[PDF](https://arxiv.org/abs/2207.05525)] |
-| Lithography Hotspot Detection Based on Heterogeneous Federated Learning With Local Adaptation and Feature Selection | ZJU | TCAD | 2024 | [PUB](https://ieeexplore.ieee.org/document/10319303) [PDF](https://arxiv.org/abs/2107.04367) |
+| Lithography Hotspot Detection Based on Heterogeneous Federated Learning With Local Adaptation and Feature Selection | ZJU | TCAD | 2024 | [[PUB](https://ieeexplore.ieee.org/document/10319303)] [[PDF](https://arxiv.org/abs/2107.04367)] |
 | FedComp: A Federated Learning Compression Framework for Resource-Constrained Edge Computing Devices | HIT | TCAD | 2024 | [[PUB](https://ieeexplore.ieee.org/document/10226409)] |
 | User-Distribution-Aware Federated Learning for Efficient Communication and Fast Inference | ECNU; SHU | TC | 2024 | [[PUB](https://ieeexplore.ieee.org/document/10308406)] |
 | FedRFQ: Prototype-Based Federated Learning With Reduced Redundancy, Minimal Failure, and Enhanced Quality | SDU | TC | 2024 | [[PUB](https://ieeexplore.ieee.org/document/10398600)] [[PDF](https://arxiv.org/abs/2401.07558)] |