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1. Peer-to-Peer Model-Agnostic Meta-Learning

   https://doi.org/10.1109/SAM60225.2024.10636527  

   Qureshi, Muhammad I; Khan, Usman A (July 2024, IEEE)
2. Peer Prediction for Learning Agents

   Feng, Shi; Yu, Fang-Yi; Chen, Yiling (December 2023, 36th Conference on Neural Information Processing Systems (NeurIPS 2022))

   Peer prediction refers to a collection of mechanisms for eliciting information from human agents when direct verification of the obtained information is unavailable. They are designed to have a game-theoretic equilibrium where everyone reveals their private information truthfully. This result holds under the assumption that agents are Bayesian and they each adopt a fixed strategy across all tasks. Human agents however are observed in many domains to exhibit learning behavior in sequential settings. In this paper, we explore the dynamics of sequential peer prediction mechanisms when participants are learning agents. We first show that the notion of no regret alone for the agents’ learning algorithms cannot guaran- tee convergence to the truthful strategy. We then focus on a family of learning algorithms where strategy updates only depend on agents’ cumulative rewards and prove that agents’ strategies in the popular Correlated Agreement (CA) mechanism converge to truthful reporting when they use algorithms from this family. This fam- ily of algorithms is not necessarily no-regret, but includes several familiar no-regret learning algorithms (e.g multiplicative weight update and Follow the Perturbed Leader) as special cases. Simulation of several algorithms in this family as well as the ε-greedy algorithm, which is outside of this family, shows convergence to the truthful strategy in the CA mechanism. 

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3. Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition

   https://doi.org/10.1109/CVPRW59228.2023.00553  

   Yuan, Liangqi; Ma, Yunsheng; Su, Lu; Wang, Ziran (June 2023, The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops)
4. How to learn collaboratively - Federated learning to peer-to-peer learning and what's at stake.

   Sharma, Atul; Zhao, Joshua; Chen, Wei; Qiu, Qiang; Bagchi, Saurabh; Chaterji, Somali (June 2023, 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), “Disrupt 23: Disruptive Ideas and New Interdisciplinary Results” Track)

   Standard ML relies on training using a centrally collected dataset, while collaborative learning techniques such as Federated Learning (FL) enable data to remain decentralized at client locations. In FL, a central server coordinates the training process, reducing computation and communication expenses for clients. However, this centralization can lead to server congestion and heightened risk of malicious activity or data privacy breaches. In contrast, Peer-to-Peer Learning (P2PL) is a fully decentralized system where nodes manage both local training and aggregation tasks. While P2PL promotes privacy by eliminating the need to trust a single node, it also results in increased computation and communication costs, along with potential difficulties in achieving consensus among nodes. To address the limitations of both FL and P2PL, we propose a hybrid approach called Hubs-and-Spokes Learning (HSL). In HSL, hubs function similarly to FL servers, maintaining consensus but exerting less control over spokes. This paper argues that HSL’s design allows for greater availability and privacy than FL, while reducing computation and communication costs compared to P2PL. Additionally, HSL maintains consensus and integrity in the learning process. 

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5. How to Learn Collaboratively - Federated Learning to Peer-to-Peer Learning and What’s at Stake

   https://doi.org/10.1109/DSN-S58398.2023.00036  

   Sharma, Atul; Zhao, Joshua C; Chen, Wei; Qiu, Qiang; Bagchi, Saurabh; Chaterji, Somali (June 2023, IEEE/IFIP DSN)