As the 5G communication networks are being widely deployed worldwide, both industry and academia have started to move beyond 5G and explore 6G communications. It is generally believed that 6G will be established on ubiquitous Artificial Intelligence (AI) to achieve data-driven Machine Learning (ML) solutions in heterogeneous and massive-scale networks. However, traditional ML techniques require centralized data collection and processing by a central server, which is becoming a bottleneck of large-scale implementation in daily life due to significantly increasing privacy concerns. Federated learning, as an emerging distributed AI approach with privacy preservation nature, is particularly attractive for various wireless applications, especially being treated as one of the vital solutions to achieve ubiquitous AI in 6G. In this article, we first introduce the integration of 6G and federated learning and provide potential federated learning applications for 6G. We then describe key technical challenges, the corresponding federated learning methods, and open problems for future research on federated learning in the context of 6G communications.

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BACKGROUNDGreater than 40% of patients with squamous cell carcinoma (SCC) of the oral cavity have lymph node metastasis at the time of diagnosis and a 5‐year survival rate of less than 50%. Changes in gene expression that regulate metastasis of SCC to lymph nodes have not been identified.METHODSTo study metastasis of oral SCC, highly metastatic oral SCC cell lines from a poorly metastatic oral SCC cell line were established by in vivo selection using a lymph node metastatic mouse model. The metastatic potential of the cells was studied using Matrigel invasion and cell surface protein adhesion assays. mRNA and protein encoded from metastasis‐related genes in the metastatic derivatives and in their parental cells were examined using Northern blot analysis, immunoblotting, rapid analysis of gene expression, and a cDNA microarray technique.RESULTSThe in vivo selected metastatic cells showed much higher Matrigel invasion capability than the parental cells. They also showed alterations in their adhesion properties to three cell surface proteins. Comparison of metastatic and nonmetastatic cells revealed several significant alterations in the expression of metastasis‐related genes, including up‐regulation of the urokinase‐type plasminogen activator receptor, integrin β1, membrane type 1‐matrix metalloproteinase, and down‐regulation of protease‐activated receptor‐1.CONCLUSONSTo the authors' knowledge, the current study is the first to report on gene expression analysis using a lymph node metastatic mouse model of human oral SCC. The data suggest that certain alterations of metastasis‐related gene expression favor invasion of oral SCC and that cell surface proteins may play major roles in the metastasis of oral SCC to the lymph nodes. Cancer 2002;95:1663–72. © 2002 American Cancer Society.DOI 10.1002/cncr.10837

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The particular features render ionic self-complementary peptide-formed and peptide amphiphile-formed nanofiber scaffolds to be compelling biomaterial substrates for nerve repair.