3 accepted papers
Federated Learning (FL) has become a prominent framework for maintaining privacy in recommender systems by enabling decentralized model training. Despite its benefits, traditional Federated Recommender Systems (FRSs)—often relying on collaborative filtering or generative models such as Variational A…
The bottleneck associated with the key-value(KV) cache presents a significant challenge during the inference processes of large language models. While depth pruning accelerates inference, it requires extensive recovery training, which can take up to two weeks. On the other hand, width pruning retain…
Federated multimedia recommendation remains unexplored due to the high dimensionality of multimedia context, which limits the federated optimization on resource-constrained user devices. To address this issue, we propose a resource-efficient and secure federated learning framework for multimedia rec…