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FLBoost: On-the-Fly Fine-tuning Boosts Federated Learning via Data-free Distillation
On the contrary, we propose a new solution: on-the-fly fine-tuning the global model in server via data-free distillation to boost its performance, dubbed FLBoost to relieve the issue of direct model aggregation.
Neighborhood Consensus Contrastive Learning for Backward-Compatible Representation
Therefore, backward-compatible representation is proposed to enable "new" features to be compared with "old" features directly, which means that the database is active when there are both "new" and "old" features in it.
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