Reliable communication algorithms have existed for a while that assumed either a global authentication model backed by public key infrastructure or peer-to-peer authentication using shared session keys between pairs of neighboring nodes. Real-life networks, however, do not settle for only one or the other. Instead, they are dynamic, heterogeneous, and often composed of a mix of authentication capabilities across different nodes.

Recent work has introduced hybrid models that combine both authenticated links and authenticated processes to better reflect these real-world conditions. These models aim to preserve the strong correctness guarantees of Byzantine-resilient protocols while reducing their communication overhead and improving practical deployability.

This thesis builds on these ideas by introducing DualRC, a reliable broadcast protocol that explicitly supports hybrid authentication environments. It also presents a novel routed version of the protocol designed to shift complexity away from intermediate nodes and toward the sender and receiver, thereby improving scalability and efficiency. Alongside the theoretical contribution, this work includes the first implementation of both variants of DualRC and evaluates their performance across a range of network conditions, trust assumptions, and deployment scenarios.

The goal of this thesis is to demonstrate that reliable communication in partially trusted networks is not only possible but can be efficient, practical, and scalable—provided that protocol design embraces the diversity and complexity of modern distributed systems.

Federated learning (FL) is a new paradigm that allows several parties to train a model together without sharing their proprietary data. This paper investigates vertical federated learning, which addresses scenarios in which collaborating organizations own data from the same set of users but with differing features. The survey provides an overview of how five alternative Vertical Federated
Learning frameworks function, as well as a description of their performance and security assurances. A thorough comparison of how each of the alternatives handles the trade-offs between data privacy, framework performance, and model performance is extracted based on the examined frameworks.
This allows the reader to form an opinion about benefits and disadvantages of various techniques across the vertical federated learning landscape.