Sustainable ai: Environmental implications, challenges and opportunities
This paper explores the environmental impact of the super-linear growth trends for AI from a
holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the …
holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the …
FELIDS: Federated learning-based intrusion detection system for agricultural Internet of Things
In this paper, we propose a federated learning-based intrusion detection system, named
FELIDS, for securing agricultural-IoT infrastructures. Specifically, the FELIDS system protects …
FELIDS, for securing agricultural-IoT infrastructures. Specifically, the FELIDS system protects …
Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout
Federated Learning (FL) has been gaining significant traction across different ML tasks,
ranging from vision to keyboard predictions. In large-scale deployments, client heterogeneity …
ranging from vision to keyboard predictions. In large-scale deployments, client heterogeneity …
PPSS: A privacy-preserving secure framework using blockchain-enabled federated deep learning for industrial IoTs
The growing reliance of industry 4.0/5.0 on emergent technologies has dramatically
increased the scope of cyber threats and data privacy issues. Recently, federated learning …
increased the scope of cyber threats and data privacy issues. Recently, federated learning …
Federated self-supervised speech representations: Are we there yet?
The ubiquity of microphone-enabled devices has lead to large amounts of unlabelled audio
data being produced at the edge. The integration of self-supervised learning (SSL) and …
data being produced at the edge. The integration of self-supervised learning (SSL) and …
DAdaQuant: Doubly-adaptive quantization for communication-efficient federated learning
Federated Learning (FL) is a powerful technique to train a model on a server with data from
several clients in a privacy-preserving manner. FL incurs significant communication costs …
several clients in a privacy-preserving manner. FL incurs significant communication costs …
An energy and carbon footprint analysis of distributed and federated learning
Classical and centralized Artificial Intelligence (AI) methods require moving data from
producers (sensors, machines) to energy hungry data centers, raising environmental …
producers (sensors, machines) to energy hungry data centers, raising environmental …
[HTML][HTML] HED-FL: A hierarchical, energy efficient, and dynamic approach for edge Federated Learning
The increasing data produced by IoT devices and the need to harness intelligence in our
environments impose the shift of computing and intelligence at the edge, leading to a novel …
environments impose the shift of computing and intelligence at the edge, leading to a novel …
Breaking physical and linguistic borders: Multilingual federated prompt tuning for low-resource languages
Pretrained large language models (LLMs) have emerged as a cornerstone in modern
natural language processing, with their utility expanding to various applications and …
natural language processing, with their utility expanding to various applications and …
End-to-end speech recognition from federated acoustic models
Training Automatic Speech Recognition (ASR) models under federated learning (FL)
settings has attracted a lot of attention recently. However, the FL scenarios often presented …
settings has attracted a lot of attention recently. However, the FL scenarios often presented …