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Found 6 papers, 1 papers with code
Fake or Compromised? Making Sense of Malicious Clients in Federated Learning
Federated learning (FL) is a distributed machine learning paradigm that enables training models on decentralized data.
Delivery Optimized Discovery in Behavioral User Segmentation under Budget Constraint
Following the discovery of segments, delivery of messages to users through preferred media channels like Facebook and Google can be challenging, as only a portion of users in a behavior segment find match in a medium, and only a fraction of those matched actually see the message (exposure).
Flow: Per-Instance Personalized Federated Learning Through Dynamic Routing
Current approaches to personalization in FL are at a coarse granularity, i. e. all the input instances of a client use the same personalized model.
EI-CLIP: Entity-Aware Interventional Contrastive Learning for E-Commerce Cross-Modal Retrieval
recommendation, and marketing services.
Federated Learning with Personalization Layers
The emerging paradigm of federated learning strives to enable collaborative training of machine learning models on the network edge without centrally aggregating raw data and hence, improving data privacy.
Data-Driven Compression of Convolutional Neural Networks
Designing and training a CNN architecture that does well on all three metrics is highly non-trivial and can be very time-consuming if done by hand.
