<p>The emergence of large foundation models (FMs) in histopathology, trained on extensive image datasets using high-performance graphics processing unit (GPU) clusters, has demonstrated significant potential in advancing computational pathology. FMs have potential to overcome the domain gap between training and testing datasets, which creates more translation opportunities. However, the reliance on vast computational resources and large-scale data often limits accessibility and widespread adoption of FMs. To address this limitation, we present <i>HistoLite</i>, a lightweight self-supervised learning framework designed to enable domain-invariant representation learning in histopathology. HistoLite utilizes customizable auto-encoders within a self-supervised learning paradigm that learns generalized and transferable features in an efficient manner. We evaluated the proposed framework using breast Whole Slide Images (WSIs) and benchmarked performance with state-of-the-art FMs for domain generalization. A novel dataset was curated that is of the same tissue slides, scanned by two different scanning platforms, which allows for specific analysis of covariate shifts due to scanner bias. Aspects evaluated include the difference in embeddings across scanners using novel representation shift metrics, including a robustness index, and accuracy, which looks at performance on downstream tasks. The top performing models were UNI, Virchow2 and Prov-GigaPath, likely due to large model sizes and training datasets. In general, most FMs were found to be susceptible to scanner-bias, as shown by differences in embeddings and drop in performance on the held-out scanner. This has significant implications for real-world deployment of FMs in histopathology. HistoLite offered low representation shift in embeddings, the lowest performance drop on out-of-domain data with modest classification accuracy, indicating the smaller model may exhibit a tradeoff between accuracy and generalization.</p>