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1. Safe Permissionless Consensus

   Pu, Youer (August 2024, Cornell University)

   Nakamoto’s consensus protocol, known for operating in a permissionless model where nodes can join and leave without notice. However, it guarantees agreement only probabilistically. Is this weaker guarantee a necessary concession to the severe demands of supporting a permissionless model? This thesis shows that it is not with the Sandglass and Gorilla Sandglass protocols. Sandglass emerges as the first permissionless consensus algorithm that transcends Nakamoto’s probabilistic limitations by guaranteeing deterministic agreement and termination with probability 1, under general omission failures. It operates under a hybrid synchronous communication model, where, despite the unknown number and dynamic participation of nodes, a majority are consistently correct and synchronously connected. Further building on the framework of Sandglass, Gorilla Sandglass is the first Byzantine fault-tolerant consensus protocol that preserves deterministic agreement and termination with probability 1 within the same synchronous model adopted by Nakamoto. Gorilla addresses the limitations of Sandglass, which only tolerates benign failures, by extending its robustness to include Byzantine failures. We prove the correctness of Gorilla by mapping executions that would violate agreement or termination in Gorilla to executions in Sandglass, where we know such violations are impossible. Establishing termination proves particularly interesting, as the mapping requires reasoning about infinite executions and their probabilities 

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2. Gorilla: Safe Permissionless Byzantine Consensus

   Pu, Youer; Farahbakhsh, Ali; Alvisi, Lorenzo; Eyal, Ittay (October 2023, Leibniz international proceedings in informatics)

   Oshman, Rothem (Ed.)

   Nakamoto’s consensus protocol works in a permissionless model and tolerates Byzantine failures, but only offers probabilistic agreement. Recently, the Sandglass protocol has shown such weaker guarantees are not a necessary consequence of a permissionless model; yet, Sandglass only tolerates benign failures, and operates in an unconventional partially synchronous model. We present Gorilla Sandglass, the first Byzantine tolerant consensus protocol to guarantee, in the same synchronous model adopted by Nakamoto, deterministic agreement and termination with probability 1 in a permissionless setting. We prove the correctness of Gorilla by mapping executions that would violate agreement or termination in Gorilla to executions in Sandglass, where we know such violations are impossible. Establishing termination proves particularly interesting, as the mapping requires reasoning about infinite executions and their probabilities. 

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3. AnoFel: Supporting Anonymity for Privacy-Preserving Federated Learning

   https://doi.org/10.56553/popets-2025-0051  

   Almashaqbeh, Ghada; Ghodsi, Zahra (April 2025, Proceedings on Privacy Enhancing Technologies)

   Federated learning enables users to collaboratively train a machine learning model over their private datasets. Secure aggregation protocols are employed to mitigate information leakage about the local datasets from user-submitted model updates. This setup, however, still leaks the user participation in training, which can also be sensitive. Protecting user anonymity is even more challenging in dynamic environments where users may (re)join or leave the training process at any point of time. This paper introduces AnoFel, the first framework to support private and anonymous dynamic participation in federated learning (FL). AnoFel leverages several cryptographic primitives, the concept of anonymity sets, differential privacy, and a public bulletin board to support anonymous user registration, as well as unlinkable and confidential model update submission. Our system allows dynamic participation, where users can join or leave at any time without needing any recovery protocol or interaction. To assess security, we formalize a notion for privacy and anonymity in FL, and formally prove that AnoFel satisfies this notion. To the best of our knowledge, our system is the first solution with provable anonymity guarantees. To assess efficiency, we provide a concrete implementation of AnoFel, and conduct experiments showing its ability to support learning applications scaling to a large number of clients. For a TinyImageNet classification task with 512 clients, the client setup to join is less than 3 sec, and the client runtime for each training iteration takes a total of 8 sec, where the added overhead of AnoFel is 46% of the total runtime. We also compare our system with prior work and demonstrate its practicality. AnoFel client runtime is up to 5x faster than Truex et al., despite the added anonymity guarantee and dynamic user joining in AnoFel. Compared to Bonawitz et al., AnoFel is only 2x slower for added support for privacy in output, dynamic user joining, and anonymity. 

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4. Tracking the Adjacency Spectral Embedding for Streaming Graphs

   https://doi.org/10.1109/IEEECONF56349.2022.10051861  

   Larroca, Federico; Bermolen, Paola; Fiori, Marcelo; Marenco, Bernardo; Mateos, Gonzalo (October 2022, 2022 56th Asilomar Conference on Signals, Systems, and Computers)

   The popular Random Dot Product Graph (RDPG) generative model postulates that each node has an associated (latent) vector, and the probability of existence of an edge between two nodes is their inner-product (with variants to consider directed and weighted graphs). In any case, the latent vectors may be estimated through a spectral decomposition of the adjacency matrix, the so-called Adjacency Spectral Embedding (ASE). Assume we are monitoring a stream of graphs and the objective is to track the latent vectors. Examples include recommender systems or monitoring of a wireless network. It is clear that performing the ASE of each graph separately may result in a prohibitive computation load. Furthermore, the invariance to rotations of the inner product complicates comparing the latent vectors at different time-steps. By considering the minimization problem underlying ASE, we develop an iterative algorithm that updates the latent vectors' estimation as new graphs from the stream arrive. Differently to other proposals, our method does not accumulate errors and thus does not requires periodically re-computing the spectral decomposition. Furthermore, the pragmatic setting where nodes leave or join the graph (e.g. a new product in the recommender system) can be accommodated as well. Our code is available at https://github.com/marfiori/efficient-ASE 

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5. Optimal soft real-time semi-partitioned scheduling made simple (and dynamic)

   https://doi.org/10.1145/3356401.3356402  

   Hobbs, Clara; Tong, Zelin; Anderson, James H. (November 2019, Proceedings of the 27th International Conference on Real-Time Networks and Systems)

   Semi-partitioned scheduling is an approach to multiprocessor real-time scheduling where most tasks are fixed to processors, while a small subset of tasks is allowed to migrate. This approach offers reduced overhead compared to global scheduling, and can reduce processor capacity loss compared to partitioned scheduling. Prior work has resulted in a number of semi-partitioned scheduling algorithms, but their correctness typically hinges on a complex intertwining of offline task assignment and online execution. This brittleness has resulted in few proposed semi-partitioned scheduling algorithms that support dynamic task systems, where tasks may join or leave the system at runtime, and few that are optimal in any sense. This paper introduces EDF-sc, the first semi-partitioned scheduling algorithm that is optimal for scheduling (static) soft real-time (SRT) sporadic task systems and allows tasks to dynamically join and leave. The SRT notion of optimality provided by EDF-sc requires deadline tardiness to be bounded for any task system that does not cause over-utilization. In the event that all tasks can be assigned as fixed, EDF-sc behaves exactly as partitioned EDF. Heuristics are provided that give EDF-sc the novel ability to stabilize the workload to approach the partitioned case as tasks join and leave the system. 

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