Federated Learning (FL) is a cutting-edge technology that enables collaborative model training without sharing raw data, allowing organizations and individuals to work together on model development while protecting sensitive data.
Practical Solutions and Value
– FL reduces communication costs and integrates diverse datasets while maintaining the unique characteristics of each participant’s data.
– Data partition strategies such as Horizontal FL, Vertical FL, and Transfer Learning provide specific advantages for different scenarios.
Data Partition Strategies
– Horizontal FL: Suitable for regional branches of the same business aiming to build a richer dataset.
– Vertical FL: Involves non-competing entities with vertically partitioned data sharing overlapping data samples.
– Transfer Learning: Applicable when there is little overlap in data samples and features among multiple subjects with heterogeneous distributions.
Defending Against Label Inference Attacks
– The KD𝑘 framework, developed by researchers at the University of Pavia, relies on Knowledge Distillation (KD) and an obfuscation algorithm to enhance privacy protection.
Value and Efficacy
– Experimental findings demonstrate a notable reduction in the accuracy of label inference attacks, validating the efficacy of the proposed defense mechanism.
AI Solutions for Business
– Practical steps for companies looking to evolve with AI include identifying automation opportunities, defining KPIs, selecting AI solutions, and implementing them gradually.
Spotlight on a Practical AI Solution
– The AI Sales Bot automates customer engagement 24/7 and manages interactions across all customer journey stages, offering a transformative approach to sales processes and customer engagement.
Contact us for a free consultation:
– AI Lab in Telegram @aiscrumbot
– Twitter – @itinaicom
