Modern society is experiencing the third spring of AI, with Distributed Machine Learning (DiML) systems used daily in healthcare, mobile, computer vision, commercial activities, finance, and natural language processing. Designing DiML systems is challenging, requiring reliability, efficiency, and trustworthiness. Academia and industry are researching solutions, highlighting the need for more venues focused on DiML. Current DiML workshops often lack interest in lessons from unsuccessful experiments or new perspectives on established topics. To address this, we envision HotDiML as a platform for critical thinking, sharing both successes and failures, and fostering constructive discussions. HotDiML encourages novel directions, accepting position papers that offer fresh perspectives or critical analyses, even if not fully developed. These papers can provide valuable insights to advance the field. This workshop invites researchers and practitioners to: (i) provide insights on DiML design challenges, (ii) present preliminary results, (iii) critique other works constructively, and (iv) share lessons learned from testing hypotheses.

*Topics of Interest*
The workshop invites authors to submit papers on the following (but not limited to) topics of interest:
- Security of FL
- Privacy and anonymity in FL
- Network-based solutions for DiML
- Multi-party computation for ML
- Homomorphic encryption for DiML
- Differential privacy in FL
- Exciting and unorthodox applications of DiML
- DiML scalability
- Online learning
- Hardware solutions for enabling DiML
- Split learning
- Cloud continuum for DiML
- Theoretical findings concerning DiML and FL
- Neuro-symbolic distributed learning
- FL-based protocols for security and privacy

*Submission Type*
The workshop provides a platform to share results, valuable critiques, and broad discussion papers. Therefore, HotDiML invites researchers and practitioners to submit four different types of papers, namely:
1- position papers offering a constructive critique of state-of-the-art DiML approaches or their implementation in real-world scenarios
2- experimental evaluation papers, presenting preliminary (un)successful or unexpected results on novel ideas
3- research papers providing thorough insights on challenges and open questions regarding the design and adoption of DiML systems
4- report papers presenting valuable lessons learned while proposing and/or testing promising but unproven ideas.
The papers submitted at HotDiML should have the potential to open a new line of research for the community, where the authors can benefit from community feedback.

*Submission Guidelines*
Submissions to the Workshop must be original and unpublished and must not be submitted concurrently for publication elsewhere. All 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 at this link.
Each submission can have up to 6 pages of main text (including figures, tables) and up to 2 pages of appendices and references. Submissions exceeding this page limit or with smaller fonts will be desk-rejected without review. The review process is singe/double-blind review.
Note that the authors should adhere to ethic and professional standards of IEEE. Please refer to IEEE Code of Ethics and IEEE Policy of AI-Generated Text.