Privacy and robustness in federated learning: Attacks and defenses
As data are increasingly being stored in different silos and societies becoming more aware
of data privacy issues, the traditional centralized training of artificial intelligence (AI) models …
of data privacy issues, the traditional centralized training of artificial intelligence (AI) models …
Fast federated machine unlearning with nonlinear functional theory
Federated machine unlearning (FMU) aims to remove the influence of a specified subset of
training data upon request from a trained federated learning model. Despite achieving …
training data upon request from a trained federated learning model. Despite achieving …
Federated learning of large language models with parameter-efficient prompt tuning and adaptive optimization
Federated learning (FL) is a promising paradigm to enable collaborative model training with
decentralized data. However, the training process of Large Language Models (LLMs) …
decentralized data. However, the training process of Large Language Models (LLMs) …
Prompt certified machine unlearning with randomized gradient smoothing and quantization
The right to be forgotten calls for efficient machine unlearning techniques that make trained
machine learning models forget a cohort of data. The combination of training and unlearning …
machine learning models forget a cohort of data. The combination of training and unlearning …
Enhancing trust and privacy in distributed networks: a comprehensive survey on blockchain-based federated learning
J Liu, C Chen, Y Li, L Sun, Y Song, J Zhou… - … and Information Systems, 2024 - Springer
While centralized servers pose a risk of being a single point of failure, decentralized
approaches like blockchain offer a compelling solution by implementing a consensus …
approaches like blockchain offer a compelling solution by implementing a consensus …
Fedasmu: Efficient asynchronous federated learning with dynamic staleness-aware model update
As a promising approach to deal with distributed data, Federated Learning (FL) achieves
major advancements in recent years. FL enables collaborative model training by exploiting …
major advancements in recent years. FL enables collaborative model training by exploiting …
Heterps: Distributed deep learning with reinforcement learning based scheduling in heterogeneous environments
Deep neural networks (DNNs) exploit many layers and a large number of parameters to
achieve excellent performance. The training process of DNN models generally handles …
achieve excellent performance. The training process of DNN models generally handles …
Shapleyfl: Robust federated learning based on shapley value
Federated Learning (FL) allows clients to form a consortium to train a global model under
the orchestration of a central server while keeping data on the local client without sharing it …
the orchestration of a central server while keeping data on the local client without sharing it …
FedHiSyn: A hierarchical synchronous federated learning framework for resource and data heterogeneity
Federated Learning (FL) enables training a global model without sharing the decentralized
raw data stored on multiple devices to protect data privacy. Due to the diverse capacity of the …
raw data stored on multiple devices to protect data privacy. Due to the diverse capacity of the …
Multi-job intelligent scheduling with cross-device federated learning
Recent years have witnessed a large amount of decentralized data in various (edge)
devices of end-users, while the decentralized data aggregation remains complicated for …
devices of end-users, while the decentralized data aggregation remains complicated for …