Collaborative learning enables distributed clients to learn a shared model for prediction while keeping the training data local on each client. However, existing collaborative learning methods require fully-labeled data for training, which is inconvenient or sometimes infeasible to obtain due to the high labeling cost and the requirement of expertise. The lack of labels makes collaborative learning impractical in many realistic settings. Self-supervised learning can address this challenge by learning from unlabeled data. Contrastive learning (CL), a self-supervised learning approach, can effectively learn visual representations from unlabeled image data. However, the distributed data collected on clients are usually not independent and identically distributed (non-IID) among clients, and each client may only have few classes of data, which degrades the performance of CL and learned representations. To tackle this problem, we propose a collaborative contrastive learning framework consisting of two approaches: feature fusion and neighborhood matching, by which a unified feature space among clients is learned for better data representations. Feature fusion provides remote features as accurate contrastive information to each client for better local learning. Neighborhood matching further aligns each client’s local features to the remote features such that well-clustered features among clients can be learned. Extensive experiments show themore »
This content will become publicly available on October 17, 2023
Local Contrastive Feature Learning for Tabular Data
Contrastive self-supervised learning has been successfully used in
many domains, such as images, texts, graphs, etc., to learn features
without requiring label information. In this paper, we propose a new
local contrastive feature learning (LoCL) framework, and our theme
is to learn local patterns/features from tabular data. In order to create
a niche for local learning, we use feature correlations to create
a maximum-spanning tree, and break the tree into feature subsets,
with strongly correlated features being assigned next to each other.
Convolutional learning of the features is used to learn latent feature
space, regulated by contrastive and reconstruction losses. Experiments
on public tabular datasets show the effectiveness of the
proposed method versus state-of-the-art baseline methods.
- Publication Date:
- NSF-PAR ID:
- 10357378
- Journal Name:
- Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM ’22), October 17–21, 2022, Atlanta, GA, USA
- Sponsoring Org:
- National Science Foundation
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