Distributed Personalized Empirical Risk Minimization

This paper advocates a new paradigm Personalized Empirical Risk Minimization (PERM) to facilitate learning from heterogeneous data sources without imposing stringent constraints on computational resources shared by participating devices. In PERM, we aim to learn a distinct model for each client by learning who to learn with and personalizing the aggregation of local empirical losses by effectively estimating the statistical discrepancy among data distributions, which entails optimal statistical accuracy for all local distributions and overcomes the data heterogeneity issue. To learn personalized models at scale, we propose a distributed algorithm that replaces the standard model averaging with model shuffling to simultaneously optimize PERM objectives for all devices. This also allows us to learn distinct model architectures (e.g., neural networks with different numbers of parameters) for different clients, thus confining underlying memory and compute resources of individual clients. We rigorously analyze the convergence of the proposed algorithm and conduct experiments that corroborate the effectiveness of the proposed paradigm

Over-expression of NOTCH1 as a biomarker for invasive breast ductal carcinoma

Breast cancer is the leading cause of cancer-related death in women worldwide. Invasive ductal carcinoma (IDC) is the most frequent invasive form of breast cancer followed by metastasis. There is no accepted marker for distinguishing this form from other less aggressive forms of breast cancer. Therefore, finding new markers especially molecularly detectable ones are noteworthy. It has been shown that NOTCH1 has been overexpressed in the patients with breast cancer, but no study has investigated the expression of NOTCH1 and its correlation with other molecular and hormonal markers of breast cancer so far. In the current study, 20 breast cancer tissues and 20 matched adjacent normal breast tissue from breast cancer patients were obtained and categorized in two groups: patients with IDC and patient with other types of breast cancer. Gene expression analysis using real-time PCR showed that the NOTCH1 gene was significantly overexpressed in patients with IDC. We also found a slight correlation between NOTCH1 overexpression and p53 accumulation in the cancerous cells confirmed by Immunohistochemistry (IHC). This results showed that it is possible to introduce NOTCH1 expression as a novel biomarker of IDC, alone or preferably accompanied by IHC of p53. We also can design new therapeutic agents targeting NOTCH1 expression for inhibition of metastasis in ductal breast carcinoma