Next Wednesday (Nov 15), at 1pm UTC, I will be giving a talk at Federated Learning One World Seminar (FLOW) about "The Future of #Consumer #EdgeAI #Computing". Tune in if you want to hear more on how we envision the next gen of the consumer device ecosystem. Paper pre-print: arxiv.org/abs/2210.10514

📢: The 111th FLOW talk is on Wednesday (15th November) at **1 pm UTC**. Steve Laskaridis (Brave) will discuss "The Future of Consumer Edge-AI Computing." Please register to our mailing list: bit.ly/3WVplLU.

📢: The 112th FLOW talk is on Wednesday (7th February) at **1 pm UTC**. Eduard Gorbunov (MBZUAI) will discuss "Variance Reduction is an Antidote to Byzantines." Please register to our mailing list: bit.ly/3WVplLU.

📢: The 113th FLOW talk is on Wednesday (21st February) at **5 pm UTC**. Shiqiang Wang (IBM Research) will discuss "How to Make Federated Learning Work with Challenging Client Participation Patterns?" Please register to our mailing list: bit.ly/3WVplLU.

📢: The 114th FLOW talk is on Wednesday (6th March) at **5 pm UTC**. Xinyi Xu (NUS) will discuss "Fairness and Incentives for Data Sharing and Collaborative Learning." Please register to our mailing list: bit.ly/3WVplLU.

📢: The 115th FLOW talk is on Wednesday (11th March) at **5 pm UTC**. Mariel Werner (UC Berkeley) will discuss "Provably Personalized and Robust Federated Learning ." Please register to our mailing list: bit.ly/3WVplLU.

📢: The 116th FLOW talk is on Wednesday (27th March) at **1 pm UTC**. Grigory Malinovsky (KAUST) will discuss "Byzantine Robustness and Partial Participation Can Be Achieved Simultaneously." Please register to our mailing list: bit.ly/3WVplLU.

📢: The 117th FLOW talk is on Wednesday (12th June) at **5 pm UTC**. Kumar Kshitij Patel (TTIC) will discuss "Revisiting the Convergence Theory for Local SGD and the Role of Personalization." Please register to our mailing list: bit.ly/3WVplLU.

📢: The 118th FLOW talk is on Wednesday (10th July) at **1 pm UTC**. Xiaowen Jiang (CISPA) will discuss "Fast Proximal-Point methods for Federated Optimization." Please register to our mailing list: bit.ly/3WVplLU.

📢: The 119th FLOW talk is on Wednesday (17th July) at **1 pm UTC**. Geovani Rizk (EPFL) will discuss "Overcoming the Challenges of Batch Normalization in Federated Learning." Please register for our mailing list at bit.ly/3WVplLU.

📢: The 120th FLOW talk is on Wednesday (9th October) at **5 pm UTC**. Nic Lane (Cambridge ML Systems Lab, Flower) will discuss "The Future of Large Language Models (and AI) is Federated." Please register for our mailing list at bit.ly/3WVplLU.

📢: The 121st FLOW talk is on Wednesday (16th October) at **1 pm UTC**. Eduard Gorbunov (MBZUAI) will discuss "FL Can Find Friends That Are Advantageous and Help with Low-Recourse Machine Translation." Please register for our mailing list at bit.ly/3WVplLU.

📢: The 122nd FLOW talk is on Wednesday (23rd October) at **5 pm UTC**. Ziyue Xu (NVIDIA AI) will discuss "Federated Learning in The Age of Foundation Models." Please register for our mailing list at bit.ly/3WVplLU.

Fully funded PhD and MSc scholarships at MBZUAI!

📢: The 123rd FLOW talk is on Wednesday (March 26) at **1 pm UTC.** Constantin Philippenko (INRIA) will discuss "Compressed and distributed least-squares regression: convergence rates." Please register for our mailing list: bit.ly/3WVplLU.

📢: The 124th FLOW talk is on Wednesday (April 9) at **1 pm UTC.** Please register for our mailing list: bit.ly/3WVplLU. FLOW Calendar: bit.ly/3E9iIT4.

📢: The 125th FLOW talk is on Wednesday (April 16) at **1 pm UTC.** Please register for our mailing list: bit.ly/3WVplLU. FLOW Calendar: bit.ly/3E9iIT4.

📢: The 126th FLOW talk is on Wednesday (April 30) at **1 pm UTC.** Please register for our mailing list: bit.ly/3WVplLU. FLOW Calendar: bit.ly/3E9iIT4.

📢: The 127th FLOW talk is on Wednesday (May 21) at **1 pm UTC.** Please register for our mailing list: bit.ly/3WVplLU. FLOW Calendar: bit.ly/3E9iIT4.

Our research group at the University of Zurich (Switzerland) is seeking a PhD candidate in intersection of theory and practice in areas such as distributed optimization, federated learning, machine learning, privacy, or unlearning. Apply here! apply.mnf.uzh.ch/positiondetail…