M3-Embedding is a single model for multi-lingual, multi-functional, and multi-granular text embeddings trained via self-knowledge distillation that achieves new state-of-the-art results on multilingual, cross-lingual, and long-document retrieval benchmarks.
Ua-fedrec: Untargeted attack on federated news recommendation
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EnCAgg filters malicious gradients in federated learning by projecting updates to two divergent dimensions for density clustering, generating boundary pseudo-gradients to link outliers, and re-clustering to recover benign updates even with unknown variable attackers.
citing papers explorer
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M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation
M3-Embedding is a single model for multi-lingual, multi-functional, and multi-granular text embeddings trained via self-knowledge distillation that achieves new state-of-the-art results on multilingual, cross-lingual, and long-document retrieval benchmarks.
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EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoning
EnCAgg filters malicious gradients in federated learning by projecting updates to two divergent dimensions for density clustering, generating boundary pseudo-gradients to link outliers, and re-clustering to recover benign updates even with unknown variable attackers.