Federated Multi-Class Learning with Positive-Only Labels
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Solution Overview
Problem
Conventional federated learning algorithms are not applicable in scenarios where users have access to only positive labels due to the inability to communicate the full model and lack of negative class embeddings, leading to trivial solutions where all instances and classes collapse to a single point in the embedding space.
Innovation Solution
Implement Federated Averaging with Spreadout (FedAwS), which includes a geometric regularizer to encourage class embeddings to be spread out in the embedding space, ensuring separation among class embeddings during training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional federated learning algorithms are used with only positive labels, then user privacy is preserved and communication of sensitive information is reduced, but the model training collapses to a trivial solution where all instances and classes converge to a single point in the embedding space
Solution Approach 1:
The patent applies preliminary anti-action by introducing a spreadout regularization term that proactively prevents class embeddings from collapsing together. This regularization term acts in opposition to the natural tendency of positive-label-only training to pull all embeddings toward a single point, thereby maintaining class separation throughout the training process
Solution Approach 2:
The patent modifies the optimization objective by adding a spreadout regularization parameter that changes the behavior of the embedding space. This parameter controls the degree of separation enforced between class embeddings, allowing the system to balance between fitting positive examples and maintaining class discrimination without requiring negative examples
2Reliability
If the server communicates only class embedding vectors to each user for privacy preservation, then user data security is improved, but the server cannot enforce class embedding separation without access to negative class embeddings
Solution Approach 1:
The patent introduces spreadout regularization as an intermediary mechanism that enables the server to enforce class embedding separation without directly communicating negative class embeddings to users. This intermediary regularization term acts as a proxy for the missing negative example information, allowing the server to maintain control over embedding separation while preserving user privacy
Solution Approach 2:
The server modifies the optimization parameters by adding a spreadout regularization term to the loss function that each user minimizes locally. This parameter change enables the server to indirectly control class embedding separation through aggregated gradients, achieving precision in embedding separation without compromising security by sharing sensitive negative example data
3Reliability
If users train models with only positive training examples, then data privacy is maintained, but the loss function only encourages small distances between instances and positive classes leading to embedding collapse
Solution Approach 1:
The patent applies preliminary anti-action by incorporating a spreadout regularization term that counteracts the collapsing force of positive-label-only training. This regularization term is introduced beforehand to prevent the degenerate solution, ensuring that instances do not all converge to the same point in the embedding space while users still only access their private positive examples
Solution Approach 2:
The spreadout regularization serves as an intermediary that bridges the gap between privacy-preserving local training and global embedding space structure. It mediates between the users' local positive examples and the global requirement for class separation, enabling effective training with positive labels only by introducing this additional structural constraint
Data Source
AI summary
Generally, the present disclosure is directed to systems and methods that perform spreadout regularization to enable learning of a multi-class classification model in the federated setting, where each user has access to the positive data associated with only a limited number of classes (e.g., a single class). Examples of such settings include decentralized training of face recognition models or speaker identification models, where in addition to the user specific facial images and voice samples, the class embeddings for the users also constitute sensitive information that cannot be shared with other users.