Semi-Supervised Vertical Federated Learning Representation Extraction
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Solution Overview
Problem
Current Vertical Federated Learning (VFL) approaches rely heavily on labeled data and fail to effectively utilize large amounts of unlabeled data, leading to poor generalization and high costs associated with data labeling, especially in scenarios where data is vertically partitioned across multiple parties.
Innovation Solution
A method where clients pre-train representation networks on unlabeled data without communication, then send representations to a server for supervised learning, allowing the server to train a prediction model, thereby reducing communication overhead and improving generalization without sharing gradients or unlabeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If clients send all unlabeled data to the server for training, then model generalization improves, but communication costs and data privacy risks increase
Solution Approach 1:
The patent extracts only the essential training components (representations and labels) from the complete unlabeled data, sending only these extracted elements to the server. This resolves the contradiction by achieving model training improvement without transmitting the entire dataset, thereby reducing communication overhead while maintaining generalization benefits.
Solution Approach 2:
The patent performs preliminary processing at the client side by pre-computing representations from unlabeled data before transmission. This preliminary action filters and prepares data locally, allowing the server to receive only processed representations rather than raw unlabeled data, thus improving generalization while minimizing communication costs.
2Measurement precision
If more labeled data is collected for training, then model accuracy improves, but labeling costs increase
Solution Approach 1:
The patent performs unsupervised pre-training on unlabeled data before supervised fine-tuning on labeled data. This preliminary unsupervised learning phase extracts useful representations from abundant unlabeled data, reducing the amount of expensive labeled data needed to achieve high model accuracy.
Solution Approach 2:
The patent changes the training paradigm from purely supervised learning to a two-stage process combining unsupervised pre-training followed by supervised fine-tuning. This parameter change in the learning approach allows effective utilization of unlabeled data, reducing dependency on large volumes of labeled data while maintaining or improving model accuracy.
3Reliability
If clients compute and share gradients during VFL training, then model training improves, but data privacy and security risks increase
Solution Approach 1:
The patent extracts and transmits only the final representations and labels from client data, avoiding the transmission of intermediate gradients that could reveal sensitive information. This extraction approach maintains model training effectiveness while eliminating privacy risks associated with gradient sharing.
Solution Approach 2:
The patent introduces representations as an intermediary between raw client data and the server model. These representations serve as a mediating structure that preserves training utility while obscuring sensitive information, allowing model improvement without direct gradient exchange that could compromise data privacy.
Data Source
AI summary
A Vertical Federated Learning system with multiple parties and a server where feature space is partitioned across multiple parties, and training includes unsupervised representation learning at each party alone followed by coordinated training with all parties and the server. Parties train representation networks on unlabeled data without communicating with other parties, then send representations of their labeled feature sets to the server, and the server trains a prediction model on labeled data without further communication with parties. Parties train representation networks on unlabeled data without communicating with other parties, then the parties and server collaboratively train the representation networks and a prediction model on labeled data. Parties alternate between training their representation networks on unlabeled data without communication and sending their representations of labeled data to the server for training a prediction model.