20 July, 2025, Glasgow
Colocated with ICDCS 2025
The DICG'25 workshop is co-located with IEEE ICDCS 2025, which takes place on July 20th - 23rd, 2025 in Glasgow, Scotland, UK.
This workshop is focused on distributed infrastructures that enable human interactions and economic activity in general with a focus on the common good. Daily life is transitioning to digital infrastructures, including friendships, education, employment, health-care, finances, family connections, and more. These infrastructures can contribute to the common good enabling us to work together to improve the wellbeing of people in our society and the wider world.
Private ownership of infrastructures does not seem to solve the traditional problems of Tragedy of Commons: pollution (spam and bot network on social media), over-exhaustion of resources (net neutrality), and fairness (gig economy). Privatization of digital commons also introduces the potential for monopolistic abuse, such as: stifled innovation, price discriminations, and distorted market knowledge discovery. We aim to explore within this workshop viable alternatives to 'winner-takes-all' platform ecosystems. Failure of market mechanisms to address these issues suggest that such infrastructures could be treated as commons. We recognize the promising avenue of research build on Nobel laureate Ostroms idea that commons is the third way to organize complex human cooperation, beyond capitalist regulation or governmental regulations.
Scientific challenges include, but are not limited to: the Tragedy of the Commons in such shared-resource systems, fake identities with Sybil attacks, robot economy, trustworthiness in general, self-organizing machine learning, market infrastructures in cashless society, and governance issues in decentralized systems.
This workshop focuses on the tools, frameworks, and algorithms to support the common good in a distributed environment. Both theoretical work and experimental approaches are welcomed. Reproducibility, open source and public datasets are endorsed.
The topics of interest include, but are not limited to:
All paper submissions should follow the IEEE 8.5" x 11" two-column format using 10pt fonts and the IEEE Conference template (downloadable by selecting "Conferences" in the IEEE-Template Selector. Each submission can have up to seven (7) pages (including figures, tables, appendices, and references). All submitted papers will be judged through single-blind reviewing.
Please submit your manuscripts at https://easychair.org/my/conference?conf=icdcsw2025.
All accepted papers will appear in an ICDCS 2025 companion proceedings, which will be available in the IEEE Digital Library prior to the workshop. At least one of the authors will have to register for the workshop and present the paper.
All times are specified in British Summer Time (BST, local time in Glasgow).
There is a strong momentum towards data-driven services at all layers of society and industry. This started from large scale web-based applications such as Web search engines (e.g., Google, Bing), social networks (e.g., Facebook, TikTok, Twitter, Instagram) and recommender systems (e.g., Amazon, Netflix) and is becoming increasingly pervasive thanks to the adoption of handheld devices and the advent of the Internet of Things. Recent initiatives such as Web 3.0 are coming with the promise of decentralising such services for empowering users with the ability to gain back control over their personal data, and prevent a few economic actors from over concentrating decision power. However decentralising online services calls for decentralising the data and the machine learning algorithms on which they heavily rely. While Federated Learning allows training machine learning models over decentralised data, it still relies on the centralised computation of model aggregations. In this presentation I will present recent research works targeting the decentralisation of machine learning beyond the well know Federated Learning concept. I will particularly focus on fundamental questions such as whether decentralisation increases or reduces the attack surface and whether decentralisation may improve personalisation compared to classical centralised cloud-based approaches.
Sonia Ben Mokhtar is a CNRS research director at the LIRIS laboratory, Lyon, France and the head of the distributed systems and information retrieval group (DRIM). She received her PhD in 2007 from Université Pierre et Marie Curie before spending two years at University College London (UK). Her research focuses on the design of resilient and privacy-preserving distributed systems. Sonia has co-authored 80+ papers in peer-reviewed conferences and journals and has served on the editorial board of IEEE Transactions on Dependable and Secure Computing and co-chaired major conferences in the field of distributed systems (e.g., ACM Middleware, IEEE DSN). Sonia has served as chair of ACM SIGOPS France and as co-chair of GDR RSD a national academic network of researchers in distributed systems and networks.
All presentation timeslots include 5 minutes of Q&A.
All presentation timeslots include 5 minutes of Q&A.
General Co-chairs:
Organization Chairs:
Program Committee: