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Federated Learning (FL) has become an established technique to facilitate privacy-preserving collaborative training across a multitude of clients.
Qsgd: Communication-efficient sgd via gradient quantization and encoding
Dan Alistarh, Demjan Grubic, et al · 2017
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Communication-Efficient Learning of Deep Networks from Decentralized Data
Brendan McMahan, Eider Moore, et al · 2017
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To prune, or not to prune: exploring the efficacy of pruning for model compression, 2017
Michael Zhu and Suyog Gupta · 2017
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Adaptive quantization for deep neural network
Yiren Zhou, Seyed Moosavi-Dezfooli, et al · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
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Substra: a framework for privacy-preserving, traceable and collaborative machine learning, 2019
Mathieu N Galtier and Camille Marini · 2019
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Parameter-efficient transfer learning for nlp, 2019
Neil Houlsby, Andrei Giurgiu, et al · 2019
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Distributed learning with compressed gradient differences, 2019
Konstantin Mishchenko, Eduard Gorbunov, et al · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, et al · 2019
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Sparse tensor core: Algorithm and hardware co-design for vector-wise sparse neural networks on modern gpus
Maohua Zhu, Tao Zhang, et al · 2019
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Federated learning: A survey on enabling technologies, protocols, and applications
Mohammed Aledhari, Rehma Razzak, Reza M. Parizi, and Fahad Saeed · 2020
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Flower: A friendly federated learning research framework, 2020
Daniel J. Beutel, Taner Topal, et al · 2020
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Heterofl: Computation and communication efficient federated learning for heterogeneous clients, 2020
Enmao Diao, Jie Ding, et al · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale, 2020
Alexey Dosovitskiy, Lucas Beyer, et al · 2020
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Fedml: A research library and benchmark for federated machine learning, 2020
Chaoyang He, Songze Li, et al · 2020
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Lotteryfl: Personalized and communication-efficient federated learning with lottery ticket hypothesis on non-iid datasets, 2020
Ang Li, Jingwei Sun, et al · 2020
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Ibm federated learning: an enterprise framework white paper v0.1, 2020
Heiko Ludwig, Nathalie Baracaldo, et al · 2020
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Federated adversarial domain adaptation
Xingchao Peng, Zijun Huang, et al · 2020
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Audio-visual model distillation using acoustic images
Andres Perez, Valentina Sanguineti, et al · 2020
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Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization
Amirhossein Reisizadeh, Aryan Mokhtari, et al · 2020
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Movement pruning: Adaptive sparsity by fine-tuning
Victor Sanh, Thomas Wolf, and Alexander Rush · 2020
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On the opportunities and risks of foundation models, 2021
Rishi Bommasani, Drew A. Hudson, et al · 2021
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Distiller: A systematic study of model distillation methods in natural language processing, 2021
Haoyu He, Xingjian Shi, et al · 2021
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FjORD: Fair and accurate federated learning under heterogeneous targets with ordered dropout
Samuel Horváth, Stefanos Laskaridis, et al · 2021
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Lora: Low-rank adaptation of large language models, 2021
Edward J. Hu, Yelong Shen, et al · 2021
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Block pruning for faster transformers
Francois Lagunas, Ella Charlaix, et al · 2021
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The power of scale for parameter-efficient prompt tuning, 2021
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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Fate: An industrial grade platform for collaborative learning with data protection
Yang Liu, Tao Fan, et al · 2021
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Linear convergence in federated learning: Tackling client heterogeneity and sparse gradients
Aritra Mitra, Rayana Jaafar, George J. Pappas, and Hamed Hassani · 2021
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Federated learning for internet of things: A comprehensive survey
Dinh C. Nguyen, Ming Ding, et al · 2021
Fate-llm: A industrial grade federated learning framework for large language models, 2023
Tao Fan, Yan Kang, et al · 2023
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Tensorflow federated: Machine learning on decentralized data
Inc. Google · 2023
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Knowledge distillation in vision transformers: A critical review, 2023
Gousia Habib, Tausifa Jan Saleem, and Brejesh Lall · 2023
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Federatedscope-llm: A comprehensive package for fine-tuning large language models in federated learning, 2023
Weirui Kuang, Bingchen Qian, et al · 2023
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A survey on federated learning systems: Vision, hype and reality for data privacy and protection
Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, and Bingsheng He · 2023
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Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models, 2021
Elad Ben Zaken, Shauli Ravfogel, and Yoav Goldberg · 2021
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A survey on federated learning
Chen Zhang, Yu Xie, et al · 2021
Cited alongside, same era.
PySyft: A Library for Easy Federated Learning
Alexander Ziller, Andrew Trask, et al · 2021
Cited alongside, same era.
Federated learning review: Fundamentals, enabling technologies, and future applications
Syreen Banabilah, Moayad Aloqaily, et al · 2022
Cited alongside, same era.
Openfl: the open federated learning library
Patrick Foley, Micah J Sheller, et al · 2022
Cited alongside, same era.
Distributed pruning towards tiny neural networks in federated learning, 2022
Hong Huang, Lan Zhang, et al · 2022
Cited alongside, same era.
Shayne Longpre, Le Hou, et al · 2023
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Fedclip: Fast generalization and personalization for clip in federated learning, 2023
Wang Lu, Xixu Hu, et al · 2023
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GPT-4 technical report, 2023
OpenAI · 2023
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The refinedweb dataset for falcon llm: Outperforming curated corpora with web data, and web data only, 2023
Guilherme Penedo, Quentin Malartic, et al · 2023
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Federated full-parameter tuning of billion-sized language models with communication cost under 18 kilobytes, 2023
Zhen Qin, Daoyuan Chen, et al · 2023
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Swarm parallelism: Training large models can be surprisingly communication-efficient, 2023
Max Ryabinin, Tim Dettmers, et al · 2023
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Exploring parameter-efficient fine-tuning for improving communication efficiency in federated learning, 2023
Guangyu Sun, Matias Mendieta, et al · 2023
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Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, et al · 2023
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Federated fine-tuning of llms on the very edge: The good, the bad, the ugly, 2023
Herbert Woisetschläger, Alexander Erben, et al · 2023
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Federatedscope: A flexible federated learning platform for heterogeneity
Yuexiang Xie, Zhen Wang, et al · 2023
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Federated learning of gboard language models with differential privacy
Zheng Xu, Yanxiang Zhang, et al · 2023
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Uniaudio: An audio foundation model toward universal audio generation, 2023
Dongchao Yang, Jinchuan Tian, et al · 2023
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Green federated learning, 2023
Ashkan Yousefpour, Shen Guo, et al · 2023
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Federated foundation models: Privacy-preserving and collaborative learning for large models, 2023
Sixing Yu, J. Pablo Muñoz, and Ali Jannesari · 2023
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Towards building the federated gpt: Federated instruction tuning, 2023
Jianyi Zhang, Saeed Vahidian, et al · 2023
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FedPETuning: When federated learning meets the parameter-efficient tuning methods of pre-trained language models
Zhuo Zhang, Yuanhang Yang, et al · 2023
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Secrets of rlhf in large language models part i: Ppo, 2023
Rui Zheng, Shihan Dou, et al · 2023
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When foundation model meets federated learning: Motivations, challenges, and future directions, 2023
Weiming Zhuang, Chen Chen, and Lingjuan Lyu · 2023
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Dual-personalizing adapter for federated foundation models
Yiyuan Yang, Guodong Long, Taoshu Shen, Jing Jiang, and Michael Blumenstein · 2024
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