Search Results for author:
Found 13 papers, 6 papers with code
Impacts of Color and Texture Distortions on Earth Observation Data in Deep Learning
In this work we systematically examine model sensitivities with respect to several color- and texture-based distortions on the input EO data during inference, given models that have been trained without such distortions.
Concept-aware clustering for decentralized deep learning under temporal shift
Decentralized deep learning requires dealing with non-iid data across clients, which may also change over time due to temporal shifts.
Grammatical gender in Swedish is predictable using recurrent neural networks
The grammatical gender of Swedish nouns is a mystery.
Efficient Node Selection in Private Personalized Decentralized Learning
Personalized decentralized learning is a promising paradigm for distributed learning, enabling each node to train a local model on its own data and collaborate with other nodes to improve without sharing any data.
EFFGAN: Ensembles of fine-tuned federated GANs
This is an even harder problem when the data is decentralized over several clients in a federated learning setup, as problems such as client drift and non-iid data make it hard for federated averaging to converge.
Decentralized adaptive clustering of deep nets is beneficial for client collaboration
We study the problem of training personalized deep learning models in a decentralized peer-to-peer setting, focusing on the setting where data distributions differ between the clients and where different clients have different local learning tasks.
Adaptive Expert Models for Personalization in Federated Learning
Federated Learning (FL) is a promising framework for distributed learning when data is private and sensitive.
Decentralized federated learning of deep neural networks on non-iid data
We tackle the non-convex problem of learning a personalized deep learning model in a decentralized setting.
Scaling Federated Learning for Fine-tuning of Large Language Models
We perform an extensive sweep over the number of clients, ranging up to 32, to evaluate the impact of distributed compute on task performance in the federated averaging setting.
Federated learning using mixture of experts
In this paper, we propose a federated learning framework using a mixture of experts to balance the specialist nature of a locally trained model with the generalist knowledge of a global model in a federated learning setting.
Specialized federated learning using a mixture of experts
In federated learning, clients share a global model that has been trained on decentralized local client data.
Adversarial representation learning for private speech generation
The model is trained in two steps: first to filter sensitive information in the spectrogram domain, and then to generate new and private information independent of the filtered one.
Adversarial representation learning for synthetic replacement of private attributes
Data privacy is an increasingly important aspect of many real-world Data sources that contain sensitive information may have immense potential which could be unlocked using the right privacy enhancing transformations, but current methods often fail to produce convincing output.
