Federated Learning (FL) has made it possible to learn from data that would’ve otherwise not been possible due to privacy and security restrictions. FL relies on each client’s honest participation and contribution. The existence of free-rider attackers may undermine the FL process allowing the free-riders to enjoy the contributions of the honest clients without any of their own. In FL algorithms such as Federated Averaging (FedAvg), the goal of the free-rider is to obtain the global model. However, algorithms such as Federated Model Distillation (FedMD) do not have a global model. This enables clients to train models with unique architectures. Here, collaborative learning is done by sharing prediction logits on a common public dataset aggregated by a central server. In this article, we propose a free-riding attack specific to this scenario. Here, the goal of the free-rider is to steal the aggregated logits from the server. Our proposed free-rider attack exploits the model heterogeneity property of FedMD and utilizes fake models to create the prediction logits. Furthermore, we improve upon FedMD and develop a KL-divergence-based detection mechanism to defend against such fake model attacks. Our experiments show that our mechanism can remove such free-riders at the very start of the FL process. Additionally, we also provide theoretical justification for the covertness of the fake model attack and the effectiveness of our detection mechanism.

Deploying applications on hybrid clouds with computational artifacts distributed over public backends and private edges involve several constraints. Designing such deployment requires application architects to solve several challenges, spanning over hard regulatory policy constraints as well as business policy constraints such as enablement of privacy by on-prem processing of data to the extent the business wants, backend support of privacy enabling technologies (PET), sustainability in terms of green energy utilization, latency sensitivity of the application. In this paper, we propose to optimize hybrid cloud application architectures, while taking all those factors into consideration, and empirically demonstrate the effectiveness of our approach. To the best of our knowledge, this work is the first of its kind.