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Found 11 papers, 2 papers with code
Local Differential Privacy for Smart Meter Data Sharing
In this paper, we propose a novel LDP approach (named LDP-SmartEnergy) that utilizes randomized response techniques with sliding windows to facilitate the sharing of appliance-level energy consumption data over time while not revealing individual users' appliance usage patterns.
Decentralised Governance-Driven Architecture for Designing Foundation Model based Systems: Exploring the Role of Blockchain in Responsible AI
Foundation models including large language models (LLMs) are increasingly attracting interest worldwide for their distinguished capabilities and potential to perform a wide variety of tasks.
Profiler: Profile-Based Model to Detect Phishing Emails
The Profiler does not require large data sets to train on to be effective and its analysis of varied email features reduces the impact of concept drift.
Decision Models for Selecting Federated Learning Architecture Patterns
Federated machine learning is growing fast in academia and industries as a solution to solve data hungriness and privacy issues in machine learning.
Global Convolutional Neural Processes
Latent neural process, a member of NPF, is believed to be capable of modelling the uncertainty on certain points (local uncertainty) as well as the general function priors (global uncertainties).
Blockchain-based Trustworthy Federated Learning Architecture
To enhance the accountability and fairness of federated learning systems, we present a blockchain-based trustworthy federated learning architecture.
FLRA: A Reference Architecture for Federated Learning Systems
The proposed FLRA reference architecture is based on an extensive review of existing patterns of federated learning systems found in the literature and existing industrial implementation.
Simeon -- Secure Federated Machine Learning Through Iterative Filtering
Federated learning enables a global machine learning model to be trained collaboratively by distributed, mutually non-trusting learning agents who desire to maintain the privacy of their training data and their hardware.
Architectural Patterns for the Design of Federated Learning Systems
Therefore, in this paper, we present a collection of architectural patterns to deal with the design challenges of federated learning systems.
A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective
Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates.
Integrating and querying similar tables from PDF documents using deep learning
Tabular data extraction from reports and other published data in PDF format is of interest for various data consolidation purposes such as analysing and aggregating financial reports of a company.
