The recent developments in the field of federated learning and distributed computing highlight a significant shift towards enhancing privacy, efficiency, and robustness in machine learning models. A common theme across the latest research is the integration of federated learning frameworks with innovative techniques to address challenges such as data privacy, communication efficiency, and model utility. Notably, advancements in Federated Online Learning to Rank (FOLTR) and Federated Unlearning (FU) demonstrate a concerted effort to improve privacy-preserving mechanisms without compromising on the effectiveness of learning models. Additionally, the exploration of distributed hybrid sketching for $ell_2$-embeddings and structured codes for distributed matrix multiplication reflects a growing interest in optimizing computational and communication efficiency in distributed environments. The introduction of Asynchronous Federated Clustering (AFCL) and Federated Deep Subspace Clustering (FDSC) further underscores the field's progression towards handling complex, non-IID data distributions and unknown cluster structures in a privacy-conscious manner. These developments collectively signify a move towards more secure, efficient, and adaptable machine learning systems that can operate effectively in distributed and privacy-sensitive settings.nn### Noteworthy Papersn- Effective and secure federated online learning to rank: Introduces a comprehensive study on FOLTR, addressing its effectiveness, robustness, security, and unlearning capabilities.n- Federated Unlearning with Gradient Descent and Conflict Mitigation: Proposes FedOSD, a novel approach to efficiently unlearn client data while mitigating model utility reduction.n- Distributed Hybrid Sketching for $ell_2$-Embeddings: Explores hybrid sketching techniques to enhance embedding results and communication efficiency in distributed settings.n- Asynchronous Federated Clustering with Unknown Number of Clusters: Presents AFCL, a method to handle asynchronous client communication and unknown cluster numbers in federated clustering.n- FedCod: An Efficient Communication Protocol for Cross-Silo Federated Learning with Coding: Introduces FedCod, a protocol that enhances communication efficiency in cross-silo FL through a coding mechanism.n- Federated Deep Subspace Clustering: Develops FDSC, a federated learning approach for subspace clustering that preserves local neighborhood relationships.n- Structured Codes for Distributed Matrix Multiplication: Establishes bounds on the optimal sum-rate for distributed computing of bilinear functions, introducing novel structured polynomial codes.n- Maximally Extendable Product Codes are Good Coboundary Expanders: Investigates the coboundary expansion property of product codes, contributing to the construction of good quantum LDPC codes.