Summary of Thinking Forward: Memory-efficient Federated Finetuning Of Language Models, by Kunjal Panchal et al.
Thinking Forward: Memory-Efficient Federated Finetuning of Language Modelsby Kunjal Panchal, Nisarg Parikh, Sunav Choudhary, Lijun…
Summary of Dager: Exact Gradient Inversion For Large Language Models, by Ivo Petrov and Dimitar I. Dimitrov et al.
DAGER: Exact Gradient Inversion for Large Language Modelsby Ivo Petrov, Dimitar I. Dimitrov, Maximilian Baader,…
Summary of Towards Client Driven Federated Learning, by Songze Li et al.
Towards Client Driven Federated Learningby Songze Li, Chenqing ZhuFirst submitted to arxiv on: 24 May…
Summary of Fedcal: Achieving Local and Global Calibration in Federated Learning Via Aggregated Parameterized Scaler, by Hongyi Peng et al.
FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scalerby Hongyi Peng,…
Summary of Decaf: Data Distribution Decompose Attack Against Federated Learning, by Zhiyang Dai et al.
Decaf: Data Distribution Decompose Attack against Federated Learningby Zhiyang Dai, Chunyi Zhou, Anmin FuFirst submitted…
Summary of Recurrent Early Exits For Federated Learning with Heterogeneous Clients, by Royson Lee et al.
Recurrent Early Exits for Federated Learning with Heterogeneous Clientsby Royson Lee, Javier Fernandez-Marques, Shell Xu…
Summary of Overcoming the Challenges Of Batch Normalization in Federated Learning, by Rachid Guerraoui et al.
Overcoming the Challenges of Batch Normalization in Federated Learningby Rachid Guerraoui, Rafael Pinot, Geovani Rizk,…
Summary of Variational Bayes For Federated Continual Learning, by Dezhong Yao et al.
Variational Bayes for Federated Continual Learningby Dezhong Yao, Sanmu Li, Yutong Dai, Zhiqiang Xu, Shengshan…
Summary of Rehearsal-free Federated Domain-incremental Learning, by Rui Sun et al.
Rehearsal-free Federated Domain-incremental Learningby Rui Sun, Haoran Duan, Jiahua Dong, Varun Ojha, Tejal Shah, Rajiv…
Summary of Cg-fedllm: How to Compress Gradients in Federated Fune-tuning For Large Language Models, by Huiwen Wu et al.
CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Modelsby Huiwen Wu, Xiaohan…