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Publication search results
found 22 matches
- 2024
- Paul Joe Maliakel, Shashikant Ilager, Ivona Brandic:
FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN. EdgeSys@EuroSys 2024: 1-6 - Neil Hurley, Erika Duriakova, James Geraci, Diarmuid O'Reilly-Morgan, Elias Z. Tragos, Barry Smyth, Aonghus Lawlor:
ALS Algorithm for Robust and Communication-Efficient Federated Learning. EuroMLSys@EuroSys 2024: 56-64 - Zeling Zhang, Dongqi Cai, Yiran Zhang, Mengwei Xu, Shangguang Wang, Ao Zhou:
FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission. EuroMLSys@EuroSys 2024: 126-133 - Pau-Chen Cheng, Kevin Eykholt, Zhongshu Gu, Hani Jamjoom, K. R. Jayaram, Enriquillo Valdez, Ashish Verma:
DeTA: Minimizing Data Leaks in Federated Learning via Decentralized and Trustworthy Aggregation. EuroSys 2024: 219-235 - Cheng-Wei Ching, Xin Chen, Taehwan Kim, Bo Ji, Qingyang Wang, Dilma Da Silva, Liting Hu:
Totoro: A Scalable Federated Learning Engine for the Edge. EuroSys 2024: 182-199 - Zhifeng Jiang, Wei Wang, Ruichuan Chen:
Dordis: Efficient Federated Learning with Dropout-Resilient Differential Privacy. EuroSys 2024: 472-488 - Ahmad Faraz Khan, Azal Ahmad Khan, Ahmed M. Abdelmoniem, Samuel Fountain, Ali Raza Butt, Ali Anwar:
FLOAT: Federated Learning Optimizations with Automated Tuning. EuroSys 2024: 200-218 - 2023
- Ahmed M. Abdelmoniem, Atal Narayan Sahu, Marco Canini, Suhaib A. Fahmy:
REFL: Resource-Efficient Federated Learning. EuroSys 2023: 215-232 - Norah Alballa, Marco Canini:
A First Look at the Impact of Distillation Hyper-Parameters in Federated Knowledge Distillation. EuroMLSys@EuroSys 2023: 123-130 - Dongqi Cai, Yaozong Wu, Haitao Yuan, Shangguang Wang, Felix Xiaozhu Lin, Mengwei Xu:
Towards Practical Few-shot Federated NLP. EuroMLSys@EuroSys 2023: 42-48 - Alex Iacob, Pedro Porto Buarque de Gusmão, Nicholas D. Lane:
Can Fair Federated Learning Reduce the need for Personalisation? EuroMLSys@EuroSys 2023: 131-139 - Chenyang Ma, Xinchi Qiu, Daniel J. Beutel, Nicholas D. Lane:
Gradient-less Federated Gradient Boosting Tree with Learnable Learning Rates. EuroMLSys@EuroSys 2023: 56-63 - Ousmane Touat, Sara Bouchenak:
Towards Robust and Bias-free Federated Learning. EuroMLSys@EuroSys 2023: 49-55 - 2022
- Ahmed M. Abdelmoniem, Chen-Yu Ho, Pantelis Papageorgiou, Marco Canini:
Empirical analysis of federated learning in heterogeneous environments. EuroMLSys@EuroSys 2022: 1-9 - Hongrui Shi, Valentin Radu:
Data selection for efficient model update in federated learning. EuroMLSys@EuroSys 2022: 72-78 - 2021
- Ahmed M. Abdelmoniem, Marco Canini:
Towards Mitigating Device Heterogeneity in Federated Learning via Adaptive Model Quantization. EuroMLSys@EuroSys 2021: 96-103 - Hongrui Shi, Valentin Radu:
Towards Federated Learning with Attention Transfer to Mitigate System and Data Heterogeneity of Clients. EdgeSys@EuroSys 2021: 61-66 - Yuanli Wang, Joel Wolfrath, Nikhil Sreekumar, Dhruv Kumar, Abhishek Chandra:
Accelerated Training via Device Similarity in Federated Learning. EdgeSys@EuroSys 2021: 31-36 - 2020
- Bram van Berlo, Aaqib Saeed, Tanir Ozcelebi:
Towards federated unsupervised representation learning. EdgeSys@EuroSys 2020: 31-36 - Angelo Feraudo, Poonam Yadav, Vadim Safronov, Diana Andreea Popescu, Richard Mortier, Shiqiang Wang, Paolo Bellavista, Jon Crowcroft:
CoLearn: enabling federated learning in MUD-compliant IoT edge networks. EdgeSys@EuroSys 2020: 25-30 - Stacey Truex, Ling Liu, Ka Ho Chow, Mehmet Emre Gursoy, Wenqi Wei:
LDP-Fed: federated learning with local differential privacy. EdgeSys@EuroSys 2020: 61-66 - 2018
- Milosz Pacholczyk, Krzysztof Rzadca:
Fair non-monetary scheduling in federated clouds. CrossCloud@EuroSys 2018: 3:1-3:6
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