To solve this challenging task, we propose a blockchain-based federated learning framework that provides collaborative data training solutions by coordinating multiple hospitals to train and share encrypted federated models without leakage of data privacy.
In this paper, a primer survey on the GML framework is provided for researchers.
Specifically for malware detection task, (i) we propose a novel user (local) neural network (LNN) which trains on local distribution and (ii) then to assure the model authenticity and quality, we propose a novel smart contract which enable aggregation process over blokchain platform.
Vehicle Re-identification (re-id) over surveillance camera network with non-overlapping field of view is an exciting and challenging task in intelligent transportation systems (ITS).
The word segmentation is a fundamental and inevitable prerequisite for many languages.
Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization.
Clustering short text streams is a challenging task due to its unique properties: infinite length, sparse data representation and cluster evolution.
Our intrinsic evaluation results demonstrate the high quality of our generated Sindhi word embeddings using SG, CBoW, and GloVe as compare to SdfastText word representations.