Federated learning (FL) has become an emerging distributed framework to build deep learning models with collaborative efforts from multiple participants. Consequently, copyright protection of FL deep model is urgently required because too many participants have access to the joint-trained model. Recently, encrypted FL framework is developed to address data leakage issue when central node is not fully trustable. This encryption process has made existing DL model watermarking schemes impossible to embed watermark at the central node. In this paper, we propose a novel clientside federated learning watermarking method to tackle the model verification issue under the encrypted FL framework. In specific, we design a backdoor-based watermarking sc...
Federated learning (FL) provides convenience for cross-domain machine learning applications and has ...
The Federated Learning method was developed to to provide an alternative for the recent concerns wit...
Federated Learning (FL) is a collaborative machine learning approach allowing participants to jointl...
Federated learning (FL) has become an emerging distributed framework to build deep learning models w...
Federated learning (FL) allows a set of agents to collaboratively train a model without sharing thei...
Distributed machine learning has been widely used in recent years to tackle the large and complex da...
Federated Learning (FL) has been envisioned as a promising approach for collaboratively training lea...
Federated learning (FL) is a popular distributed machine learning paradigm which enables jointly tra...
As an emerging training model with neural networks, federated learning has received widespread atten...
Federated Learning (FL) enables collaborative training of Deep Learning (DL) models where the data i...
AI\u27s applicability across diverse fields is hindered by data sensitivity, privacy concerns, and l...
Federated learning (FL) that enables edge devices to collaboratively learn a shared model while keep...
Federated learning, as one of the three main technical routes for privacy computing, has been widely...
Federated learning (FL) enables multiple clients to jointly train a global learning model while keep...
Abstract Federated learning is a privacy-aware collaborative machine learning method, but it needs o...
Federated learning (FL) provides convenience for cross-domain machine learning applications and has ...
The Federated Learning method was developed to to provide an alternative for the recent concerns wit...
Federated Learning (FL) is a collaborative machine learning approach allowing participants to jointl...
Federated learning (FL) has become an emerging distributed framework to build deep learning models w...
Federated learning (FL) allows a set of agents to collaboratively train a model without sharing thei...
Distributed machine learning has been widely used in recent years to tackle the large and complex da...
Federated Learning (FL) has been envisioned as a promising approach for collaboratively training lea...
Federated learning (FL) is a popular distributed machine learning paradigm which enables jointly tra...
As an emerging training model with neural networks, federated learning has received widespread atten...
Federated Learning (FL) enables collaborative training of Deep Learning (DL) models where the data i...
AI\u27s applicability across diverse fields is hindered by data sensitivity, privacy concerns, and l...
Federated learning (FL) that enables edge devices to collaboratively learn a shared model while keep...
Federated learning, as one of the three main technical routes for privacy computing, has been widely...
Federated learning (FL) enables multiple clients to jointly train a global learning model while keep...
Abstract Federated learning is a privacy-aware collaborative machine learning method, but it needs o...
Federated learning (FL) provides convenience for cross-domain machine learning applications and has ...
The Federated Learning method was developed to to provide an alternative for the recent concerns wit...
Federated Learning (FL) is a collaborative machine learning approach allowing participants to jointl...