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Federated Learning (FL), while a breakthrough in decentralized machine learning, contends with significant challenges such as limited data availability and the variability of computational resources, which can stifle the performance and scalability of the models.
Fedmd: Heterogenous federated learning via model distillation
Daliang Li and Junpu Wang · 2019
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Client selection for federated learning with heterogeneous resources in mobile edge
Takayuki Nishio and Ryo Yonetani · 2019
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Ensemble distillation for robust model fusion in federated learning
Tao Lin, Lingjing Kong, Sebastian U. Stich, and Martin Jaggi · 2020
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Joint device scheduling and resource allocation for latency constrained wireless federated learning
Wenqi Shi, Sheng Zhou, Zhisheng Niu, Miao Jiang, and Lu Geng · 2020
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Vulnerabilities in federated learning
Nader Bouacida and Prasant Mohapatra · 2021
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FAIR: quality-aware federated learning with precise user incentive and model aggregation
Yongheng Deng, Feng Lyu, Ju Ren, Yi-Chao Chen, Peng Yang, Yuezhi Zhou, and Yaoxue Zhang · 2021
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A survey on federated learning for resource-constrained iot devices
Ahmed Imteaj, Urmish Thakker, Shiqiang Wang, Jian Li, and M Hadi Amini · 2021
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Federated learning for internet of things: Recent advances, taxonomy, and open challenges
Latif U Khan, Walid Saad, Zhu Han, Ekram Hossain, and Choong Seon Hong · 2021
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Fedcav: contribution-aware model aggregation on distributed heterogeneous data in federated learning
Hui Zeng, Tongqing Zhou, Yeting Guo, Zhiping Cai, and Fang Liu · 2021
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Data-free knowledge distillation for heterogeneous federated learning
Zhuangdi Zhu, Junyuan Hong, and Jiayu Zhou · 2021
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Leveraging asynchronous federated learning to predict customers financial distress
Ahmed Imteaj and M Hadi Amini · 2022
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Contribution-aware federated learning for smart healthcare
Zelei Liu, Yuanyuan Chen, Yansong Zhao, Han Yu, Yang Liu, Renyi Bao, Jinpeng Jiang, Zaiqing Nie, Qian Xu, and Qiang Yang · 2022
Cited alongside, same era.
Towards federated covid-19 vaccine side effect prediction
Jiaqi Wang, Cheng Qian, Suhan Cui, Lucas Glass, and Fenglong Ma · 2022
Cited alongside, same era.
An efficient federated distillation learning system for multitask time series classification
Huanlai Xing, Zhiwen Xiao, Rong Qu, Zonghai Zhu, and Bowen Zhao · 2022
Cited alongside, same era.
Federated learning for the internet of things: Applications, challenges, and opportunities
Tuo Zhang, Lei Gao, Chaoyang He, Mi Zhang, Bhaskar Krishnamachari, and A Salman Avestimehr · 2022
Cited alongside, same era.
Deep generative models for synthetic data: A survey
Peter Eigenschink, Thomas Reutterer, Stefan Vamosi, Ralf Vamosi, Chang Sun, and Klaudius Kalcher · 2023
Cited alongside, same era.
Privacy-preserving on-screen activity tracking and classification in e-learning using federated learning
Durjoy Mistry, Muhammad Firoz Mridha, Mejdl Safran, Sultan Alfarhood, Aloke Kumar Saha, and Dunren Che · 2023
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Responsible and effective federated learning in financial services: A comprehensive survey
Yueyue Shi, Hengjie Song, and Jun Xu · 2023
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Openai styple api
Minghui Tian · 2023
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Instructions as backdoors: Backdoor vulnerabilities of instruction tuning for large language models
Jiashu Xu, Mingyu Derek Ma, Fei Wang, Chaowei Xiao, and Muhao Chen · 2023
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Knowledge distillation from multiple foundation models for end-to-end speech recognition
Xiaoyu Yang, Qiujia Li, Chao Zhang, and Philip C. Woodland · 2023
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Christian Fachola, Agustín Tornaría, Paola Bermolen, Germán Capdehourat, Lorena Etcheverry, and María Inés Fariello · 2023
Cited alongside, same era.
Get the best ai prompt services
Fiverr · 2023
Cited alongside, same era.
Decentralized federated learning through proxy model sharing
Shivam Kalra, Junfeng Wen, Jesse C Cresswell, Maksims Volkovs, and HR Tizhoosh · 2023
Cited alongside, same era.
Backdoor threats from compromised foundation models to federated learning
Xi Li, Songhe Wang, Chen Wu, Hao Zhou, and Jiaqi Wang · 2023
Cited alongside, same era.
Fedclip: Fast generalization and personalization for clip in federated learning
Wang Lu, Xixu Hu, Jindong Wang, and Xing Xie · 2023
Cited alongside, same era.
Decodingtrust: A comprehensive assessment of trustworthiness in GPT models
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, and Bo Li
Cited in the paper.
Knowledge-enhanced semi-supervised federated learning for aggregating heterogeneous lightweight clients in iot
Jiaqi Wang, Shenglai Zeng, Zewei Long, Yaqing Wang, Houping Xiao, and Fenglong Ma
Cited in the paper.
Fedgh: Heterogeneous federated learning with generalized global header
Liping Yi, Gang Wang, Xiaoguang Liu, Zhuan Shi, and Han Yu · 2023
Later among the works it cites.
Gpt-fl: Generative pre-trained model-assisted federated learning
Tuo Zhang, Tiantian Feng, Samiul Alam, Dimitrios Dimitriadis, Mi Zhang, Shrikanth S Narayanan, and Salman Avestimehr · 2023
Later among the works it cites.
When foundation model meets federated learning: Motivations, challenges, and future directions
Weiming Zhuang, Chen Chen, and Lingjuan Lyu · 2023
Later among the works it cites.
Feddat: An approach for foundation model finetuning in multi-modal heterogeneous federated learning
Haokun Chen, Yao Zhang, Denis Krompass, Jindong Gu, and Volker Tresp · 2024
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Unveiling backdoor risks brought by foundation models in heterogeneous federated learning
Xi Li, Chen Wu, and Jiaqi Wang · 2024
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