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This article discusses the challenges faced by current Machine Learning approaches and proposes a paradigm shift towards a decentralized and trustless architecture for privacy-aware Graph Representation Learning. The use of Gossip Learning and other gossip-based peer-to-peer techniques is suggested to achieve scalability and resilience while reducing the risk of privacy leaks. The article also identifies three key research directions to achieve this vision and presents the contributions made in each direction.
