Zobrazeno 1 - 10
of 109
pro vyhledávání: '"Guler, Basak"'
Autor:
Chang, Xiangyu, Ahmed, Sk Miraj, Krishnamurthy, Srikanth V., Guler, Basak, Swami, Ananthram, Oymak, Samet, Roy-Chowdhury, Amit K.
The key premise of federated learning (FL) is to train ML models across a diverse set of data-owners (clients), without exchanging local data. An overarching challenge to this date is client heterogeneity, which may arise not only from variations in
Externí odkaz:
http://arxiv.org/abs/2402.08769
Autor:
Chang, Xiangyu, Ahmed, Sk Miraj, Krishnamurthy, Srikanth V., Guler, Basak, Swami, Ananthram, Oymak, Samet, Roy-Chowdhury, Amit K.
Parameter-efficient tuning (PET) methods such as LoRA, Adapter, and Visual Prompt Tuning (VPT) have found success in enabling adaptation to new domains by tuning small modules within a transformer model. However, the number of domains encountered dur
Externí odkaz:
http://arxiv.org/abs/2401.04130
Secure aggregation is a popular protocol in privacy-preserving federated learning, which allows model aggregation without revealing the individual models in the clear. On the other hand, conventional secure aggregation protocols incur a significant c
Externí odkaz:
http://arxiv.org/abs/2112.12872
Autor:
So, Jinhyun, He, Chaoyang, Yang, Chien-Sheng, Li, Songze, Yu, Qian, Ali, Ramy E., Guler, Basak, Avestimehr, Salman
Secure model aggregation is a key component of federated learning (FL) that aims at protecting the privacy of each user's individual model while allowing for their global aggregation. It can be applied to any aggregation-based FL approach for trainin
Externí odkaz:
http://arxiv.org/abs/2109.14236
Publikováno v:
AAAI 2023
Secure aggregation is a critical component in federated learning (FL), which enables the server to learn the aggregate model of the users without observing their local models. Conventionally, secure aggregation algorithms focus only on ensuring the p
Externí odkaz:
http://arxiv.org/abs/2106.03328
Autor:
Guler, Basak, Yener, Aylin
Potential environmental impact of machine learning by large-scale wireless networks is a major challenge for the sustainability of future smart ecosystems. In this paper, we introduce sustainable machine learning in federated learning settings, using
Externí odkaz:
http://arxiv.org/abs/2102.11274
Autor:
Guler, Basak, Yener, Aylin
This paper provides a first study of utilizing energy harvesting for sustainable machine learning in distributed networks. We consider a distributed learning setup in which a machine learning model is trained over a large number of devices that can h
Externí odkaz:
http://arxiv.org/abs/2102.05639
We consider a collaborative learning scenario in which multiple data-owners wish to jointly train a logistic regression model, while keeping their individual datasets private from the other parties. We propose COPML, a fully-decentralized training fr
Externí odkaz:
http://arxiv.org/abs/2011.01963
Secure federated learning is a privacy-preserving framework to improve machine learning models by training over large volumes of data collected by mobile users. This is achieved through an iterative process where, at each iteration, users update a gl
Externí odkaz:
http://arxiv.org/abs/2007.11115
Federated learning is a distributed framework for training machine learning models over the data residing at mobile devices, while protecting the privacy of individual users. A major bottleneck in scaling federated learning to a large number of users
Externí odkaz:
http://arxiv.org/abs/2002.04156