Federated Learning (FL) is a decentralized machine learning approach that provides a privacy-friendly way of training models by keeping the datasets of participating parties private. Some challenges FL faces are the lack of incentives to encourage participation in the learning process, as well as preventing potential cyber attacks that tamper with the model. Blockchain is an available solution that provides the means to implement incentives to encourage participation and issue penalties to disincentivize malicious behavior. Hence, recent developments introduced blockchain-enabled FL (BCFL) designs for various applications. However, one obstacle that slows down the widespread adoption of this technology is the high latency of blockchain networks due to its laborious consensus protocols. In this paper, we propose StateFL, a revised BCFL architecture that uses state channels (a blockchain scaling solution) in order to ease the load on the blockchain by reducing the number of on-chain transactions, improving the system’s latency, and minimizing transaction fees as a result. State channels are governed by smart contracts and enable two parties to exchange information and assets off-chain unless disputes occur. Either channel party can dispute the state of the channel if suspicious behavior is observed. In that case, the dispute is settled on the blockchain. We evaluate StateFL in a series of experiments to establish latency improvements, identify bottlenecks, and quantify the impact of disputes on channel latency and transaction fees. The findings show that the higher the number of FL rounds, the more StateFL outperforms the baseline BCFL, with the exception of a very low number of rounds. In realistic FL scenarios, the rounds are in the order of hundreds making StateFL a solid contender even if disputes do occasionally occur. The bottleneck of StateFL is the channel setup and closure which require extensive interaction with the blockchain.

Anonymity networks, such as The Invisible Internet Project, commonly known as I2P, enable privacy aware users to stay anonymous on the Internet and provide secure methods of communication, as well as multi-layered encryption. Despite the many innocent reasons users opt for online anonymity, these particular networks are censored at times, as they are associated with criminal activity. The goal of this paper is to measure to what extent I2P network users are being blocked by popular websites, and not, however, by governments or internet service providers. To establish this, we developed a web crawler which compares the responses to HTTP(S) GET requests sent anonymously, via I2P, and non-anonymously. Our results are based on the analysis of the received HTTP status codes, and on screenshots of the requested websites, to assess content blocking. This experiment shows that I2P users suffer from some form of blocking in 10.09% of cases. However, it should be noted that I2P faces certain bandwidth limitations and traffic congestion at the outproxy. This is a result of the fact that I2P was not designed with the intent of being a proxy to the Internet, but rather a self sustaining peer-to-peer network.