We use a 5G network traffic dataset to simulate concept drift and test various scenarios. In this paper, we show that using Attention in Federated Learning (FL) is an efficient way of handling concept drifts. This problem is more pronounced in a distributed setting where multiple ML models are being used for different heterogeneous datasets and the final model needs to capture all concept drifts. The ML models can present high error rates while the drifts take place and the errors decrease only after the model learns the distributions. The sudden changes in data distributions caused by changing scenarios (e.g., 5G base station failures) is referred to as concept drift and is a major challenge to continual learning. For ML to provide the best solutions, it is important to continually train the ML models to include the changing scenarios. Various studies have demonstrated that ML is highly suitable for optimizing edge computing systems as rapid mobility and application-induced changes occur at the edge. Machine learning (ML) is expected to play a major role in 5G edge computing.
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Through experiments on three real datasets implementing two use cases: a location check-in recommendation and a movie recommendation, we demonstrate that our solution converges up to 42% faster than with other decentralized solutions providing up to 9% improvement on average performance metric such as hit ratio and up to 21% improvement on long tail performance compared to decentralized competitors. At the heart of PEPPER reside two key components: a personalized peer-sampling protocol that keeps in the neighborhood of each node, a proportion of nodes that have similar interests to the former and a simple yet effective model aggregation function that builds a model that is better suited to each user. In PEPPER, users gossip model updates and aggregate them asynchronously. To remedy this, we propose PEPPER, a decentralized recommender system based on gossip learning principles. However, FL, and therefore FL-based recommender systems, rely on a central server that can experience scalability issues besides being vulnerable to attacks. In this context, recommender systems based on Federated Learning (FL) appear to be a promising solution for enforcing privacy as they compute accurate recommendations while keeping personal data on the users' devices.
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etc.), which exposes users to numerous privacy threats. However, to be effective, these systems need to collect and analyze large volumes of personal data (e.g., location check-ins, movie ratings, click rates. Recommender systems are proving to be an invaluable tool for extracting user-relevant content helping users in their daily activities (e.g., finding relevant places to visit, content to consume, items to purchase).
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We present and discuss the design of TRIBLER, and we show evidence that TRIBLER enables fast content discovery and recommendation at a low additional overhead, and a significant improvement in download performance. Based on this paradigm's main concepts such as taste buddies and friends, we have designed and implemented the TRIBLER P2P file-sharing system as a set of extensions to BitTorrent.
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In this paper we present a novel social-based P2P file-sharing paradigm that exploits social phenomena by maintaining social networks and using these in content discovery, content recommendation, and downloading. However, social phenomena such as friendship and the existence of communities of users with similar tastes or interests may well be exploited in such systems in order to increase their usability and performance. Most current peer-to-peer (P2P) file-sharing systems treat their users as anonymous, unrelated entities, and completely disregard any social relationships between them.