Three papers at NeurIPS’23

Our group had a fun week at NeurIPS’23 presenting three papers:

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 "A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks”

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"SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models”
Best Paper at FL@FM-NeurIPS’23

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 "FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System”