Summary of Flash: Federated Learning-based Llms For Advanced Query Processing in Social Networks Through Rag, by Sai Puppala et al.
FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAGby Sai Puppala,…
Summary of Advancing Oncology with Federated Learning: Transcending Boundaries in Breast, Lung, and Prostate Cancer. a Systematic Review, by Anshu Ankolekar et al.
Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic…
Summary of Lid-fl: Towards List-decodable Federated Learning, by Hong Liu et al.
LiD-FL: Towards List-Decodable Federated Learningby Hong Liu, Liren Shan, Han Bao, Ronghui You, Yuhao Yi,…
Summary of Privacy-preserved Taxi Demand Prediction System Utilizing Distributed Data, by Ren Ozeki et al.
Privacy-Preserved Taxi Demand Prediction System Utilizing Distributed Databy Ren Ozeki, Haruki Yonekura, Hamada Rizk, Hirozumi…
Summary of Overlay-based Decentralized Federated Learning in Bandwidth-limited Networks, by Yudi Huang et al.
Overlay-based Decentralized Federated Learning in Bandwidth-limited Networksby Yudi Huang, Tingyang Sun, Ting HeFirst submitted to…
Summary of Constructing Adversarial Examples For Vertical Federated Learning: Optimal Client Corruption Through Multi-armed Bandit, by Duanyi Yao et al.
Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Banditby Duanyi Yao,…
Summary of Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy, by Wei Huo et al.
Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacyby Wei Huo, Changxin Liu, Kemi Ding,…
Summary of Fedad-bench: a Unified Benchmark For Federated Unsupervised Anomaly Detection in Tabular Data, by Ahmed Anwar et al.
FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Databy Ahmed Anwar, Brian…
Summary of Tackling Noisy Clients in Federated Learning with End-to-end Label Correction, by Xuefeng Jiang et al.
Tackling Noisy Clients in Federated Learning with End-to-end Label Correctionby Xuefeng Jiang, Sheng Sun, Jia…
Summary of Fedbat: Communication-efficient Federated Learning Via Learnable Binarization, by Shiwei Li and Wenchao Xu and Haozhao Wang and Xing Tang and Yining Qi and Shijie Xu and Weihong Luo and Yuhua Li and Xiuqiang He and Ruixuan Li
FedBAT: Communication-Efficient Federated Learning via Learnable Binarizationby Shiwei Li, Wenchao Xu, Haozhao Wang, Xing Tang,…