14. IBM Federated Learning Features
Fusion Algos
• FedAvg McMahan et al.
• FedAvgPlus
• Cordinate Median
• PFNMYurochkin et al.
• Krum
• Zeno and more
Supported ML Libraries
• Sklearn
• Keras
• Tensorflow
• Pytorch
• RLlib
Supported Models
• Neural networks
• XGBoost
• Linear/Logistic
regressors/classifiers
• Decision Tree ID3
• K-means
• Reinforcement Learning
Connectivity
• Web sockets
• gRPC
• Flask
• Rabbit MQ
FL Process Mgmt
• Early termination
• Quorum support
Data Handlers
• MNIST
• FEMNIST
• Adult
• Diabetes
• CIFAR-10 and more
Others
• Crypto
• Experiment Manager
• Differential Privacy
• Fairness
* Features only available in IBM Cloud version
23. IBM FL Community Edition
23
Github: https://github.com/IBM/federated-
learning-lib
Getting Started :-
1. examples
2. Experiment Manager notebook
Web site: https://ibmfl.mybluemix.net
Slack Channel : https://ibm-fl.slack.com/
Distributes as WHL file, not open
source
Here are a subset of use cases and industries where Federated Learning is already being used. There are many use cases for Financial Services, Manufacturing, Healthcare, and Mobile Applications.
Something common across the use cases and industries are that they require Strict data privacy rules, both for regulatory and competitive reasons
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Traditionally, banks use rule-based and manual efforts to detect fraud and risk, which is prone to human error
Risky small and micro enterprise loans are an important rising indicator of bank success, without credit risk identification
Save time from banks using manual and rule-based human identification of fraud and increase accuracy which saves money and better customer experience
Webank united several banks and trained anti money laundering models jointly LINK
High, given IBM’s expertise in the area of Fraud analysis
Predictive Maintenance & Quality (Manufacturing)
Largest amount of data from sensors/IOT devices on individual machines
Data cannot be gathered fast enough in one place to do analysis
Unscheduled machine break down is a top challenge that can derail the business
Achieve high asset utilization and savings in operational costs Model trained on many factories’ data is more robust in predicting failures
ByteLake, an AI consultancy based in Poland, recently released a POC in concert with Lenovo for predictive maintenance LINK
High, as IBM offers a PMQ solution that can be applied
Patient Analytics
(Healthcare)
Cannot freely share or pool patient data due to policies like GDPR, HIPAA, CCPA etc.
Need for more complex analysis data sets, such as medical images or from medical sensors
Data is also a valuable proprietary resource for pharma/healthcare organizations
Delivers exceptional performance in deep learning while keeping patient data secure and private
Using pre-trained models and transfer learning techniques, NVIDIA AI assists radiologists in labeling, reducing the time for complex 3D studies from hours to minutes LINK
High, as IBM offers strong capabilities in medical imaging analysis (Watson) and IOT
Motivation to adopt FL
Solution Benefits
Competitive Use cases
Ability for IBM to Execute this use case