The main results achieved in the CARENET project are:
1) We have fully characterized the fundamental limits of Device-to-Device (D2D) coded caching, establishing conditions for the exact optimality of an improved scheme with respect to our previously proposed scheme (which was order-optimal but not exactly optimal) under uncoded prefetching (i.e. under the condition that the users store directly segments of the library files, and not functions thereof).
2) We have formulated a new problem of coded caching with privacy, where the privacy of the users demands must be preserved with respect to the other users in the system. In fact, in the original schemes of coded caching, every user could be aware and easily determine which file other users have demanded. In contrast, we have proposed a novel scheme to achieve information theoretic privacy of the demands and characterized its order-optimality, both for the single server topology and for the D2D topology.
3) We have significantly expanded the network topologies for which coded caching was previously studied, including novel ``Fog'' topologies with partially shared caches, coded caching over MIMO channels, multi-access coded caching for shared caches under combinatorial topologies, hierarchical networks, and two-dimensional distributed caching networks.
4) We have considered the implementation of coded caching over practical ``routing networks'', that serves as a guideline for implementing coded caching at the application layer, i.e. ``above IP''. In particular, these were: the inability of intermediate nodes such as wireless routers and base stations to store cached content; the fact that coded caching should be run at the server-client level, by some third party content distribution entity, while the core network and the wireless access are fixed by some protocol (e.g. 5G) and run by another entity (a wireless network provider); the problem of subpacketization, which must be limited for finite length files; the problem of decentralized operations, for which each user should be able to cache independently from other users; the problem of asynchronous streaming sessions, where each user starts and stop at arbitrary times.
5) We have fully characterized the problem of coded caching under different channel quality and video quality to the users. In particular, we have laid the foundations for
variable adaptive quality coded caching schemes that can work in synergy with the standard quality adaptation schemes used by state-of-the art streaming services such as YouTube and Netflix,
based on the so-called DASH approach (dynamic adaptive streaming over HTTP).
6) We have extensively tested our ideas over a physical platform for coded caching over WiFi networks built at TU Berlin in collaboration with CADAMI, a startup company based in Munich.
7) We have extended the coded caching scenarios from file delivery to more general computation tasks, i.e. the delivery of functions of data files.
We have determined the fundamental information theoretic limits of coded caching for linear function computation, where each user demands an arbitrary linear combination of the data files.
We have also considered the problem of data shuffling, where users must exchange data such that each user, at the end of the exchange, has a different data block in an assigned data block permutation.
Furthermore, we have exploited the formal similarity and common coding approaches to address problems in secure data aggregation in federated learning, which is an important paradigm of deep learning on large distributed data sets involving proprietary user data. One of the most exciting outcomes of CARENET is the discovery of the many formal analogies and coding technique similarities between a number of apparently unrelated problems, such as coded caching, private information retrieval, coded distributed computing, and secure federated learning. All together, the output of the project yields a new fundamental and unifying framework to these many problems in network coding and information theory.
