coai

$LYN has caught attention again with a strong bounce, but looking at the bigger picture, it has already seen a massive drop from its highs. Now it seems to be in a phase where it’s trying to rebuild momentum.
We’ve seen tokens like $AIA and $COAI explode after periods of accumulation, driven by hype, volume, and strong market sentiment. The question is — is $LYN entering a similar phase?
For a move like that, LYN needs:
Strong and sustained buying volume
A fresh wave of AI narrative hype
A clean breakout above recent resistance zones
Right now, the structure suggests a possible recovery, but also reminds us how quickly these tokens can reverse.
If momentum builds, it could surprise.
If not, it may continue its cycle of pumps and pullbacks.
In tokens like this, timing matters more than anything.
#LYN #EverlynAI #COAI #AIA

$COAI #COAI
We consider a project (model) owner that would like to train a model by utilizing the local private data and compute power of interested data owners, i.e., trainers. Our goal is to design a data marketplace for such decentralized collaborative/federated learning applications that simultaneously provides i) proof-of-contribution based reward allocation so that the trainers are compensated based on their contributions to the trained model; ii) privacy-preserving decentralized model training by avoiding any data movement from data owners; iii) robustness against malicious parties (e.g., trainers aiming to poison the model); iv) verifiability in the sense that the integrity, i.e., correctness, of all computations in the data market protocol including contribution assessment and outlier detection are verifiable through zero-knowledge proofs; and v) efficient and universal design. We propose a blockchain-based marketplace design to achieve all five objectives mentioned above. In our design, we utilize a distributed storage infrastructure and an aggregator aside from the project owner and the trainers. The aggregator is a processing node that performs certain computations, including assessing trainer contributions, removing outliers, and updating hyper-parameters. We execute the proposed data market through a blockchain smart contract. The deployed smart contract ensures that the project owner cannot evade payment, and honest trainers are rewarded based on their contributions at the end of training. Finally, we implement the building blocks of the proposed data market and demonstrate their applicability in practical scenarios through extensive experiments.
Source : chainopera.ai
@Yi He @Richard Teng @CZ @Binance_Angels