A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions
The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …
However, new privacy concerns have also emerged during the aggregation of the …
Federated learning for internet of things: A comprehensive survey
The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of
intelligent services and applications empowered by artificial intelligence (AI). Traditionally …
intelligent services and applications empowered by artificial intelligence (AI). Traditionally …
Federated learning meets blockchain in edge computing: Opportunities and challenges
Mobile-edge computing (MEC) has been envisioned as a promising paradigm to handle the
massive volume of data generated from ubiquitous mobile devices for enabling intelligent …
massive volume of data generated from ubiquitous mobile devices for enabling intelligent …
Distributed learning in wireless networks: Recent progress and future challenges
The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …
applications to efficiently analyze various types of data collected by edge devices for …
[HTML][HTML] Communication-efficient federated learning via knowledge distillation
Federated learning is a privacy-preserving machine learning technique to train intelligent
models from decentralized data, which enables exploiting private data by communicating …
models from decentralized data, which enables exploiting private data by communicating …
Cocktailsgd: Fine-tuning foundation models over 500mbps networks
Distributed training of foundation models, especially large language models (LLMs), is
communication-intensive and so has heavily relied on centralized data centers with fast …
communication-intensive and so has heavily relied on centralized data centers with fast …
Fedml: A research library and benchmark for federated machine learning
Federated learning (FL) is a rapidly growing research field in machine learning. However,
existing FL libraries cannot adequately support diverse algorithmic development; …
existing FL libraries cannot adequately support diverse algorithmic development; …
Federated learning in smart cities: Privacy and security survey
Over the last decade, smart cities (SC) have been developed worldwide. Implementing big
data and the internet of things improves the monitoring and integration of different …
data and the internet of things improves the monitoring and integration of different …
From distributed machine learning to federated learning: A survey
In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …
users, various regions or organizations. Because of laws or regulations, the distributed data …
Fedscale: Benchmarking model and system performance of federated learning at scale
We present FedScale, a federated learning (FL) benchmarking suite with realistic datasets
and a scalable runtime to enable reproducible FL research. FedScale datasets encompass …
and a scalable runtime to enable reproducible FL research. FedScale datasets encompass …