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Deep federated learning: a systematic review of methods, applications, and challenges

Frontiers in Computer Science
Federated Learning (FL) represents a paradigm shift in machine learning, enabling collaborative model training on decentralized data while preserving user privacy.
Lakshan Cooray, Janaka Sendanayake, Pramuditha Vithanaarachchi, Y. H. P. P. Priyadarshana   +3 more
doaj   +1 more source

Computationally efficient deniable communication

2016 IEEE International Symposium on Information Theory (ISIT), 2016
In this paper, we design the first computationally efficient codes for simultaneously reliable and deniable communication over a Binary Symmetric Channel (BSC). Our setting is as follows - a transmitter Alice wishes to potentially reliably transmit a message to a receiver Bob, while ensuring that the transmission taking place is deniable from ...
Qiaosheng Eric Zhang, Mayank Bakshi, Sidharth Jaggi   +2 more
openaire   +2 more sources

Communication Efficient Secret Sharing [PDF]

IEEE Transactions on Information Theory, 2016
submitted to the IEEE Transactions on Information Theory.
Wentao Huang, Michael Langberg, Jörg Kliewer, Jehoshua Bruck   +3 more
openaire   +3 more sources

Optimizing Client Participation in Communication-Constrained Federated LLM Adaptation with LoRA

Sensors
Federated learning (FL) enables privacy-preserving adaptation of large language models (LLMs) across distributed clients. However, deploying FL in edge environments remains challenging because of the high communication overhead of full-model updates ...
Faranaksadat Solat, Joohyung Lee
doaj   +1 more source

Communication-Efficient String Sorting [PDF]

2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2020
Full version to appear at IPDPS ...
Timo Bingmann, Peter Sanders 0001, Matthias Schimek   +2 more
openaire   +3 more sources

FedDAR: Federated Learning With Data-Quantity Aware Regularization for Heterogeneous Distributed Data

IEEE Access
Federated learning (FL) has emerged as a promising approach for collaboratively training global models and classifiers without sharing private data. However, existing studies primarily focus on distinct methodologies for typical and personalized FL (tFL ...
Youngjun Kwak, Minyoung Jung
doaj   +1 more source

Distributed SGD With Flexible Gradient Compression

IEEE Access, 2020
We design and evaluate a new algorithm called FlexCompressSGD for training deep neural networks over distributed datasets via multiple workers and a central server.
Tran Thi Phuong, Le Trieu Phong
doaj   +1 more source

Efficient RDMA Communication Protocols

CoRR, 2022
Developers of networked systems often work with low-level RDMA libraries to tailor network modules to take full advantage of offload capabilities offered by RDMA-capable network controllers. Because of the huge design space of networked data access protocols and variability in capabilities of RDMA infrastructure, developers tend to reinvent and ...
Konstantin Taranov, Fabian Fischer 0008, Torsten Hoefler   +2 more
openaire   +2 more sources

Efficient federated learning for distributed neuroimaging data

Frontiers in Neuroinformatics
Recent advancements in neuroimaging have led to greater data sharing among the scientific community. However, institutions frequently maintain control over their data, citing concerns related to research culture, privacy, and accountability. This creates
Bishal Thapaliya, Bishal Thapaliya, Riyasat Ohib, Riyasat Ohib, Eloy Geenjaar, Eloy Geenjaar, Jingyu Liu, Jingyu Liu, Vince Calhoun, Vince Calhoun, Vince Calhoun, Sergey M. Plis, Sergey M. Plis   +12 more
doaj   +1 more source

Phosphatidylinositol 4‐kinase as a target of pathogens—friend or foe?

FEBS Letters, EarlyView.
This graphical summary illustrates the roles of phosphatidylinositol 4‐kinases (PI4Ks). PI4Ks regulate key cellular processes and can be hijacked by pathogens, such as viruses, bacteria and parasites, to support their intracellular replication. Their dual role as essential host enzymes and pathogen cofactors makes them promising drug targets.
Ana C. Mendes, Guilherme M. Azevedo, Amanda P. Barcellos, Diana Bahia   +3 more
wiley   +1 more source