4 accepted papers
To achieve better performance and greater fairness in Federated Learning (FL), much of the existing research has centered on individual clients, using domain adaptation techniques and redesigned aggregation schemes to counteract client data heterogeneity. However, an overlooked scenario exists where…
Quantization is an effective approach for memory cost reduction by compressing networks to lower bits. However, existing quantization processes learned only from the current data tend to suffer from forgetting catastrophe on streaming data, i.e., significant performance decrement on old task data af…
Quantization is an efficient network compression approach to reduce the inference time. However, existing approaches ignored the distribution difference between training and testing data, thereby inducing a large quantization error in inference. To address this issue, we propose a new quantization s…
Quantization compresses models to low bits for efficient inferences which has received increasing attentions. However, existing approaches focused on balanced datasets, while imbalanced data is pervasive in the real world. Therefore, in this study, we investigate the realistic problem, quantization…