Federated AI lets a team imagine together: Federated Learning of GANs

2019-06-09 · Rajagopal. A, Nirmala. V

Envisioning a new imaginative idea together is a popular human need. Imagining together as a team can often lead to breakthrough ideas, but the collaboration effort can also be challenging, especially when the team members are separated by time and space. What if there is a AI that can assist the team to collaboratively envision new ideas?. Is it possible to develop a working model of such an AI? This paper aims to design such an intelligence. This paper proposes a approach to design a creative and collaborative intelligence by employing a form of distributed machine learning approach called Federated Learning along with fusion on Generative Adversarial Networks, GAN. This collaborative creative AI presents a new paradigm in AI, one that lets a team of two or more to come together to imagine and envision ideas that synergies well with interests of all members of the team. In short, this paper explores the design of a novel type of AI paradigm, called Federated AI Imagination, one that lets geographically distributed teams to collaboratively imagine.

📄 PDF Abstract BibTeX arXiv:1906.03595

Code (0)

등록된 구현이 없습니다.

Tasks

Federated Learning

Methods 이 논문이 사용한 방법론

Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…

Dogecoin Customer Service Number +1-833-534-1729 설명 없음

Development and Design of FLKit: A Structured Onboarding Toolkit for Federated Learning in Health and Life Sciences

2026-06-22 · Ashkan Pirmani, Ilse Vermeulen, Goran Vinterhalter, Lotte Geys 외 arxiv

Federated learning lets institutions train shared models without moving their data, which makes it a natural fit for health and life sciences research under strict privacy regulation. The methods are maturing fast, but t…

Federated Learning

Experience Constrained Hierarchical Federated Reinforcement Learning for Large-scale UAV Teams in Hazardous Environments

2026-05-04 · Qinwei Huang, Rui Zuo, Simon Khan, Qinru Qiu arxiv

Conventional federated learning assumes that greater learner participation improves training performance, by leveraging abundant, independently generated local data. However, in federated reinforcement learning (FRL) for…

Reinforcement Learning Federated Learning

Federated Learning as a Mean-Field Game

2021-07-08 · Arash Mehrjou

We establish a connection between federated learning, a concept from machine learning, and mean-field games, a concept from game theory and control theory. In this analogy, the local federated learners are considered as …

Federated Learning Privacy Preserving

Personalized Multi-tier Federated Learning

2024-07-19 · Sourasekhar Banerjee, Ali Dadras, Alp Yurtsever, Monowar Bhuyan

The key challenge of personalized federated learning (PerFL) is to capture the statistical heterogeneity properties of data with inexpensive communications and gain customized performance for participating devices. To ad…

Federated Learning Personalized Federated Learning

Federated Learning for Malware Detection in IoT Devices

2021-04-15 · Valerian Rey, Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Gérôme Bovet 외

This work investigates the possibilities enabled by federated learning concerning IoT malware detection and studies security issues inherent to this new learning paradigm. In this context, a framework that uses federated…

Federated Learning Malware Detection